Channels / User Profile
User Info
Yevhenii
| ID | 5843798463 |
|---|---|
| Type | user |
| Username | @Yevhenii_Successo |
| First Name | Yevhenii |
| First Seen | 2026-01-11 09:15 UTC |
| Updated | 2026-01-11 09:15 UTC |
Statistics
Total Messages: 17
Active in: 1 channel(s)
Activity by Channel
Recent Messages
📄 Document (not supported)
#resume #cv #data #financial #middle #middleplus #dataanalyst #financialanalyst #bi #analyst #powerbi #sql #python #excel #googlesheets #metabase #mongodb #fintech #payments #reconciliation #remote #relocation
Hi everyone,
I’m Yevhenii Chernyshev, Data / BI / Financial Analyst with 15+ years in finance and 2+ years in data analytics, including fintech, payments, reconciliation, BI, and financial data automation.
I work at the intersection of finance, operations, and data, turning raw and messy datasets into clear dashboards, automated pipelines, reconciliation logic, forecasts, and business decisions.
🔹 Core expertise
📊 Data / BI Analytics
End-to-end analytics: data extraction, transformation, modeling, dashboards, reporting automation, and data quality checks.
Built operational and financial dashboards used by management for daily decision-making.
💼 Financial & Unit Economics Analytics
P&L, Cash Flow, Budget vs Actual, forecasting, margin analysis, cost structure optimization.
Strong focus on unit economics, revenue streams, transaction flows, payment logic, and operational profitability.
🏦 Fintech / Payments / Reconciliation
Built Python / MSSQL pipelines for PSP reconciliation, transaction matching, and financial control
Rebuilt Special transaction chain logic for cascaded provider flows
Restored correct parent-child transaction matching across multiple providers
Improved reconciliation coverage from around 70-80% to near-complete matching
Automated issue reports, summary reports, exception reports, and validation checks
Investigated legacy transaction issues from 2024-2025 that had not been detected before
Worked with PSP amount logic, fee calculation, chargebacks, refunds, unmatched transactions, and amount mismatches
Built Rolling Reserve logic: MID mapping, tariff mapping, FX conversion, RR accruals, caps, release schedules, and BI-ready outputs
🚕 Operational / Logistics Analytics
Order flow, delivery performance, SLA tracking
Courier performance, distance and route metrics
Surcharges, system income vs courier income
Transaction and payment analytics
Large datasets, multi-table data models
Building DWH logic from raw operational data
🔹 Product / Business Analytics
Funnels, cohorts, retention, LTV, segmentation, KPI systems, A/B logic support.
🔹 Tech Stack
Python: pandas, numpy, matplotlib, seaborn, scikit-learn, Prophet, statsmodels
SQL: PostgreSQL, MSSQL, BigQuery, MySQL, complex joins, window functions, CTEs
BI: Power BI, Tableau, Metabase, Looker Studio
Data tools: Excel, Google Sheets, PowerQuery, VBA, AppScript
Databases: PostgreSQL, MongoDB
Other: ETL automation, REST API, JSON/XML, Git, Jupyter, Google Drive/Sheets API
🔹 Key Achievements
Built BI systems for logistics, financial, and transaction data
Designed data models and dashboards combining financial, operational, and payment metrics
Improved reconciliation coverage from around 70-80% to near-complete results
Rebuilt cascaded transaction logic and restored correct provider transaction matching
Automated reconciliation, exception reporting, Rolling Reserve calculations, and data quality controls
Identified legacy data and transaction issues affecting financial reporting and reconciliation accuracy
Regular ad-hoc deep dives into product, financial, operational, and transaction data
🔹 Languages & format
English - B2 | German - A2 | Russian - native | Ukrainian - native
Remote / full-time / B2B | Open to relocation
📎 CV: Notion CV + portfolio**
📱 TG: **@Yevhenii_successo
Hi everyone,
I’m Yevhenii Chernyshev, Data / BI / Financial Analyst with 15+ years in finance and 2+ years in data analytics, including fintech, payments, reconciliation, BI, and financial data automation.
I work at the intersection of finance, operations, and data, turning raw and messy datasets into clear dashboards, automated pipelines, reconciliation logic, forecasts, and business decisions.
🔹 Core expertise
📊 Data / BI Analytics
End-to-end analytics: data extraction, transformation, modeling, dashboards, reporting automation, and data quality checks.
Built operational and financial dashboards used by management for daily decision-making.
💼 Financial & Unit Economics Analytics
P&L, Cash Flow, Budget vs Actual, forecasting, margin analysis, cost structure optimization.
Strong focus on unit economics, revenue streams, transaction flows, payment logic, and operational profitability.
🏦 Fintech / Payments / Reconciliation
Built Python / MSSQL pipelines for PSP reconciliation, transaction matching, and financial control
Rebuilt Special transaction chain logic for cascaded provider flows
Restored correct parent-child transaction matching across multiple providers
Improved reconciliation coverage from around 70-80% to near-complete matching
Automated issue reports, summary reports, exception reports, and validation checks
Investigated legacy transaction issues from 2024-2025 that had not been detected before
Worked with PSP amount logic, fee calculation, chargebacks, refunds, unmatched transactions, and amount mismatches
Built Rolling Reserve logic: MID mapping, tariff mapping, FX conversion, RR accruals, caps, release schedules, and BI-ready outputs
🚕 Operational / Logistics Analytics
Order flow, delivery performance, SLA tracking
Courier performance, distance and route metrics
Surcharges, system income vs courier income
Transaction and payment analytics
Large datasets, multi-table data models
Building DWH logic from raw operational data
🔹 Product / Business Analytics
Funnels, cohorts, retention, LTV, segmentation, KPI systems, A/B logic support.
🔹 Tech Stack
Python: pandas, numpy, matplotlib, seaborn, scikit-learn, Prophet, statsmodels
SQL: PostgreSQL, MSSQL, BigQuery, MySQL, complex joins, window functions, CTEs
BI: Power BI, Tableau, Metabase, Looker Studio
Data tools: Excel, Google Sheets, PowerQuery, VBA, AppScript
Databases: PostgreSQL, MongoDB
Other: ETL automation, REST API, JSON/XML, Git, Jupyter, Google Drive/Sheets API
🔹 Key Achievements
Built BI systems for logistics, financial, and transaction data
Designed data models and dashboards combining financial, operational, and payment metrics
Improved reconciliation coverage from around 70-80% to near-complete results
Rebuilt cascaded transaction logic and restored correct provider transaction matching
Automated reconciliation, exception reporting, Rolling Reserve calculations, and data quality controls
Identified legacy data and transaction issues affecting financial reporting and reconciliation accuracy
Regular ad-hoc deep dives into product, financial, operational, and transaction data
🔹 Languages & format
English - B2 | German - A2 | Russian - native | Ukrainian - native
Remote / full-time / B2B | Open to relocation
📎 CV: Notion CV + portfolio**
📱 TG: **@Yevhenii_successo
📄 Document (not supported)
#resume #cv #data #financial #middle #middleplus #dataanalyst #financialanalyst #bi #analyst #powerbi #sql #python #excel #googlesheets #metabase #mongodb #fintech #payments #reconciliation #remote #relocation
Hi everyone,
I’m Yevhenii Chernyshev, Data / BI / Financial Analyst with 15+ years in finance and 2+ years in data analytics, including fintech, payments, reconciliation, BI, and financial data automation.
I work at the intersection of finance, operations, and data, turning raw and messy datasets into clear dashboards, automated pipelines, reconciliation logic, forecasts, and business decisions.
🔹 Core expertise
📊 Data / BI Analytics
End-to-end analytics: data extraction, transformation, modeling, dashboards, reporting automation, and data quality checks.
Built operational and financial dashboards used by management for daily decision-making.
💼 Financial & Unit Economics Analytics
P&L, Cash Flow, Budget vs Actual, forecasting, margin analysis, cost structure optimization.
Strong focus on unit economics, revenue streams, transaction flows, payment logic, and operational profitability.
🏦 Fintech / Payments / Reconciliation
Built Python / MSSQL pipelines for PSP reconciliation, transaction matching, and financial control
Rebuilt Special transaction chain logic for cascaded provider flows
Restored correct parent-child transaction matching across multiple providers
Improved reconciliation coverage from around 70-80% to near-complete matching
Automated issue reports, summary reports, exception reports, and validation checks
Investigated legacy transaction issues from 2024-2025 that had not been detected before
Worked with PSP amount logic, fee calculation, chargebacks, refunds, unmatched transactions, and amount mismatches
Built Rolling Reserve logic: MID mapping, tariff mapping, FX conversion, RR accruals, caps, release schedules, and BI-ready outputs
🚕 Operational / Logistics Analytics
Order flow, delivery performance, SLA tracking
Courier performance, distance and route metrics
Surcharges, system income vs courier income
Transaction and payment analytics
Large datasets, multi-table data models
Building DWH logic from raw operational data
🔹 Product / Business Analytics
Funnels, cohorts, retention, LTV, segmentation, KPI systems, A/B logic support.
🔹 Tech Stack
Python: pandas, numpy, matplotlib, seaborn, scikit-learn, Prophet, statsmodels
SQL: PostgreSQL, MSSQL, BigQuery, MySQL, complex joins, window functions, CTEs
BI: Power BI, Tableau, Metabase, Looker Studio
Data tools: Excel, Google Sheets, PowerQuery, VBA, AppScript
Databases: PostgreSQL, MongoDB
Other: ETL automation, REST API, JSON/XML, Git, Jupyter, Google Drive/Sheets API
🔹 Key Achievements
Built BI systems for logistics, financial, and transaction data
Designed data models and dashboards combining financial, operational, and payment metrics
Improved reconciliation coverage from around 70-80% to near-complete results
Rebuilt cascaded transaction logic and restored correct provider transaction matching
Automated reconciliation, exception reporting, Rolling Reserve calculations, and data quality controls
Identified legacy data and transaction issues affecting financial reporting and reconciliation accuracy
Regular ad-hoc deep dives into product, financial, operational, and transaction data
🔹 Languages & format
English - B1 | German - A2 | Russian - native | Ukrainian - native
Remote / full-time / B2B | Open to relocation
📎 CV: Notion CV + portfolio**
📱 TG: **@Yevhenii_successo
Hi everyone,
I’m Yevhenii Chernyshev, Data / BI / Financial Analyst with 15+ years in finance and 2+ years in data analytics, including fintech, payments, reconciliation, BI, and financial data automation.
I work at the intersection of finance, operations, and data, turning raw and messy datasets into clear dashboards, automated pipelines, reconciliation logic, forecasts, and business decisions.
🔹 Core expertise
📊 Data / BI Analytics
End-to-end analytics: data extraction, transformation, modeling, dashboards, reporting automation, and data quality checks.
Built operational and financial dashboards used by management for daily decision-making.
💼 Financial & Unit Economics Analytics
P&L, Cash Flow, Budget vs Actual, forecasting, margin analysis, cost structure optimization.
Strong focus on unit economics, revenue streams, transaction flows, payment logic, and operational profitability.
🏦 Fintech / Payments / Reconciliation
Built Python / MSSQL pipelines for PSP reconciliation, transaction matching, and financial control
Rebuilt Special transaction chain logic for cascaded provider flows
Restored correct parent-child transaction matching across multiple providers
Improved reconciliation coverage from around 70-80% to near-complete matching
Automated issue reports, summary reports, exception reports, and validation checks
Investigated legacy transaction issues from 2024-2025 that had not been detected before
Worked with PSP amount logic, fee calculation, chargebacks, refunds, unmatched transactions, and amount mismatches
Built Rolling Reserve logic: MID mapping, tariff mapping, FX conversion, RR accruals, caps, release schedules, and BI-ready outputs
🚕 Operational / Logistics Analytics
Order flow, delivery performance, SLA tracking
Courier performance, distance and route metrics
Surcharges, system income vs courier income
Transaction and payment analytics
Large datasets, multi-table data models
Building DWH logic from raw operational data
🔹 Product / Business Analytics
Funnels, cohorts, retention, LTV, segmentation, KPI systems, A/B logic support.
🔹 Tech Stack
Python: pandas, numpy, matplotlib, seaborn, scikit-learn, Prophet, statsmodels
SQL: PostgreSQL, MSSQL, BigQuery, MySQL, complex joins, window functions, CTEs
BI: Power BI, Tableau, Metabase, Looker Studio
Data tools: Excel, Google Sheets, PowerQuery, VBA, AppScript
Databases: PostgreSQL, MongoDB
Other: ETL automation, REST API, JSON/XML, Git, Jupyter, Google Drive/Sheets API
🔹 Key Achievements
Built BI systems for logistics, financial, and transaction data
Designed data models and dashboards combining financial, operational, and payment metrics
Improved reconciliation coverage from around 70-80% to near-complete results
Rebuilt cascaded transaction logic and restored correct provider transaction matching
Automated reconciliation, exception reporting, Rolling Reserve calculations, and data quality controls
Identified legacy data and transaction issues affecting financial reporting and reconciliation accuracy
Regular ad-hoc deep dives into product, financial, operational, and transaction data
🔹 Languages & format
English - B1 | German - A2 | Russian - native | Ukrainian - native
Remote / full-time / B2B | Open to relocation
📎 CV: Notion CV + portfolio**
📱 TG: **@Yevhenii_successo
📄 Document (not supported)
#resume #cv #data #financial #middle #middle+ #dataanalyst #financialanalyst #bi #analyst #powerbi #sql #python #excel #googlesheets #metabase #mongodb #remote #relocation
Hi everyone,
I’m Yevhenii Chernyshev — Data / BI / Financial Analyst with 15+ years in finance and 2+ years in data analytics, including operational analytics in a logistics tech company.
I work at the intersection of finance, operations, and data, turning raw, messy datasets into clear dashboards, forecasts, and business decisions.
🔹 Core expertise
📊Data / BI Analytics
End-to-end analytics: data extraction, transformation, modeling, dashboards & automation.
Built operational and financial dashboards used by management for daily decision-making.
💼 Financial & Unit Economics Analytics*
P&L, Cash Flow, Budget vs Actual, forecasting, margin analysis, cost structure optimization.
Strong focus on unit economics, revenue streams, and operational profitability.
🚕 Operational / Logistics Analytics
– Order flow, delivery performance, SLA tracking
– Courier performance, distance & route metrics
– Surcharges, system income vs courier income
– Transaction and payment analytics
– Large datasets (~10m+ orders), multi-table data models
– Building DWH logic from raw operational data
🔹Product / Business Analytics
Funnels, cohorts, retention, LTV, segmentation, KPI systems, A/B logic support.
🔹 Tech Stack
Python: pandas, numpy, matplotlib, seaborn, scikit-learn, Prophet, statsmodels
SQL: PostgreSQL, MSSQL, BigQuery, MySQL — complex joins, window functions, CTEs
BI: Power BI, Tableau, Metabase, Looker Studio
Data tools: Excel (advanced), Google Sheets, PowerQuery, VBA, AppScript
Databases: PostgreSQL, MongoDB
Other: ETL automation, REST API, JSON/XML, Git, Jupyter
🔹 Key Achievements
– Built operational BI system for logistics data (orders, couriers, payments, routes)
– Designed data models and dashboards combining financial + operational metrics
– Automated reporting across departments (finance, ops, management)
– Created forecasting models improving planning accuracy
– Identified inefficiencies that helped reduce operational costs
– Regular ad-hoc deep dives into product, financial, and operational data
🔹 Languages & format
English — B1 | German — A2 | Russian — native | Ukrainian — native
Remote / full-time / B2B | Open to relocation
📎 CV: Notion CV + portfolio**
📱 TG: **@Yevhenii_successo
Hi everyone,
I’m Yevhenii Chernyshev — Data / BI / Financial Analyst with 15+ years in finance and 2+ years in data analytics, including operational analytics in a logistics tech company.
I work at the intersection of finance, operations, and data, turning raw, messy datasets into clear dashboards, forecasts, and business decisions.
🔹 Core expertise
📊Data / BI Analytics
End-to-end analytics: data extraction, transformation, modeling, dashboards & automation.
Built operational and financial dashboards used by management for daily decision-making.
💼 Financial & Unit Economics Analytics*
P&L, Cash Flow, Budget vs Actual, forecasting, margin analysis, cost structure optimization.
Strong focus on unit economics, revenue streams, and operational profitability.
🚕 Operational / Logistics Analytics
– Order flow, delivery performance, SLA tracking
– Courier performance, distance & route metrics
– Surcharges, system income vs courier income
– Transaction and payment analytics
– Large datasets (~10m+ orders), multi-table data models
– Building DWH logic from raw operational data
🔹Product / Business Analytics
Funnels, cohorts, retention, LTV, segmentation, KPI systems, A/B logic support.
🔹 Tech Stack
Python: pandas, numpy, matplotlib, seaborn, scikit-learn, Prophet, statsmodels
SQL: PostgreSQL, MSSQL, BigQuery, MySQL — complex joins, window functions, CTEs
BI: Power BI, Tableau, Metabase, Looker Studio
Data tools: Excel (advanced), Google Sheets, PowerQuery, VBA, AppScript
Databases: PostgreSQL, MongoDB
Other: ETL automation, REST API, JSON/XML, Git, Jupyter
🔹 Key Achievements
– Built operational BI system for logistics data (orders, couriers, payments, routes)
– Designed data models and dashboards combining financial + operational metrics
– Automated reporting across departments (finance, ops, management)
– Created forecasting models improving planning accuracy
– Identified inefficiencies that helped reduce operational costs
– Regular ad-hoc deep dives into product, financial, and operational data
🔹 Languages & format
English — B1 | German — A2 | Russian — native | Ukrainian — native
Remote / full-time / B2B | Open to relocation
📎 CV: Notion CV + portfolio**
📱 TG: **@Yevhenii_successo
📄 Document (not supported)
#resume #cv #data #financial #middle #middle+ #dataanalyst #financialanalyst #bi #analyst #powerbi #sql #python #excel #googlesheets #metabase #mongodb #remote #relocation
Hi everyone,
I’m Yevhenii Chernyshev — Data / BI / Financial Analyst with 15+ years in finance and 2+ years in data analytics, including operational analytics in a logistics tech company.
I work at the intersection of finance, operations, and data, turning raw, messy datasets into clear dashboards, forecasts, and business decisions.
🔹 Core expertise
📊Data / BI Analytics
End-to-end analytics: data extraction, transformation, modeling, dashboards & automation.
Built operational and financial dashboards used by management for daily decision-making.
💼 Financial & Unit Economics Analytics*
P&L, Cash Flow, Budget vs Actual, forecasting, margin analysis, cost structure optimization.
Strong focus on unit economics, revenue streams, and operational profitability.
🚕 Operational / Logistics Analytics
– Order flow, delivery performance, SLA tracking
– Courier performance, distance & route metrics
– Surcharges, system income vs courier income
– Transaction and payment analytics
– Large datasets (~10m+ orders), multi-table data models
– Building DWH logic from raw operational data
🔹Product / Business Analytics
Funnels, cohorts, retention, LTV, segmentation, KPI systems, A/B logic support.
🔹 Tech Stack
Python: pandas, numpy, matplotlib, seaborn, scikit-learn, Prophet, statsmodels
SQL: PostgreSQL, MSSQL, BigQuery, MySQL — complex joins, window functions, CTEs
BI: Power BI, Tableau, Metabase, Looker Studio
Data tools: Excel (advanced), Google Sheets, PowerQuery, VBA, AppScript
Databases: PostgreSQL, MongoDB
Other: ETL automation, REST API, JSON/XML, Git, Jupyter
🔹 Key Achievements
– Built operational BI system for logistics data (orders, couriers, payments, routes)
– Designed data models and dashboards combining financial + operational metrics
– Automated reporting across departments (finance, ops, management)
– Created forecasting models improving planning accuracy
– Identified inefficiencies that helped reduce operational costs
– Regular ad-hoc deep dives into product, financial, and operational data
🔹 Languages & format
English — B1 | German — A2 | Russian — native | Ukrainian — native
Remote / full-time / B2B | Open to relocation
📎 CV: Notion CV + portfolio**
📱 TG: **@Yevhenii_successo
Hi everyone,
I’m Yevhenii Chernyshev — Data / BI / Financial Analyst with 15+ years in finance and 2+ years in data analytics, including operational analytics in a logistics tech company.
I work at the intersection of finance, operations, and data, turning raw, messy datasets into clear dashboards, forecasts, and business decisions.
🔹 Core expertise
📊Data / BI Analytics
End-to-end analytics: data extraction, transformation, modeling, dashboards & automation.
Built operational and financial dashboards used by management for daily decision-making.
💼 Financial & Unit Economics Analytics*
P&L, Cash Flow, Budget vs Actual, forecasting, margin analysis, cost structure optimization.
Strong focus on unit economics, revenue streams, and operational profitability.
🚕 Operational / Logistics Analytics
– Order flow, delivery performance, SLA tracking
– Courier performance, distance & route metrics
– Surcharges, system income vs courier income
– Transaction and payment analytics
– Large datasets (~10m+ orders), multi-table data models
– Building DWH logic from raw operational data
🔹Product / Business Analytics
Funnels, cohorts, retention, LTV, segmentation, KPI systems, A/B logic support.
🔹 Tech Stack
Python: pandas, numpy, matplotlib, seaborn, scikit-learn, Prophet, statsmodels
SQL: PostgreSQL, MSSQL, BigQuery, MySQL — complex joins, window functions, CTEs
BI: Power BI, Tableau, Metabase, Looker Studio
Data tools: Excel (advanced), Google Sheets, PowerQuery, VBA, AppScript
Databases: PostgreSQL, MongoDB
Other: ETL automation, REST API, JSON/XML, Git, Jupyter
🔹 Key Achievements
– Built operational BI system for logistics data (orders, couriers, payments, routes)
– Designed data models and dashboards combining financial + operational metrics
– Automated reporting across departments (finance, ops, management)
– Created forecasting models improving planning accuracy
– Identified inefficiencies that helped reduce operational costs
– Regular ad-hoc deep dives into product, financial, and operational data
🔹 Languages & format
English — B1 | German — A2 | Russian — native | Ukrainian — native
Remote / full-time / B2B | Open to relocation
📎 CV: Notion CV + portfolio**
📱 TG: **@Yevhenii_successo
📎 Webpage (not supported)
#resume #cv #data #financial #middle #middle+ #dataanalyst #financialanalyst #bi #analyst #powerbi #sql #python #excel #googlesheets #metabase #mongodb #remote #relocation
Hi everyone
I’m Yevhenii Chernyshev — Data / BI / Financial Analyst with 15+ years in finance and 2 years in analytics.
I connect finance, business, and data to help teams make better decisions.
🔹 Core areas I cover
Business / Product Analytics – metrics (DAU/MAU, LTV, Retention), A/B tests, funnels, cohort & segment analysis
Financial Analytics – P&L, Cash Flow, Budget vs Actual, Unit Economics, forecasting (Python / Power BI / Excel)
Data / BI Analytics – ETL pipelines, dashboards (Power BI, Metabase, Tableau, Looker Studio), automation, SQL optimization
Marketing / Sales Analytics – channel performance, ROMI/ROI, RFM, segmentation & sales forecasting
🔹 Stack & Tools
Python: pandas, numpy, matplotlib, seaborn, scikit-learn, Prophet, statsmodels
SQL: PostgreSQL, MSSQL, BigQuery, MySQL, SQLite — complex joins, window functions, CTEs, ad-hoc analytical queries
BI: Power BI, Tableau, Metabase, Google Looker Studio
Data: Excel, Google Sheets, PowerQuery, VBA, AppScript
Databases: MongoDB, PostgreSQL
Other: Git, Jupyter, REST API, JSON/XML, ETL automation tools
🔹 Achievements
– Automated financial and operational reporting across departments
– Built interactive Power BI & Metabase dashboards for management
– Designed SQL & MongoDB models for transaction analytics
– Created Python forecasting models improving accuracy by 20%
– Delivered insights that helped reduce operational costs by 15%
– Solved ad-hoc analytical tasks for finance, product, and operations teams (SQL + Power BI)
🔹 Languages & format
English — B1 | German — A2 | Russian — native | Ukrainian — native
Remote / full-time / B2B | Open to relocation
📎 CV: Notion CV + portfolio**
📱 TG: **@Yevhenii_successo
Hi everyone
I’m Yevhenii Chernyshev — Data / BI / Financial Analyst with 15+ years in finance and 2 years in analytics.
I connect finance, business, and data to help teams make better decisions.
🔹 Core areas I cover
Business / Product Analytics – metrics (DAU/MAU, LTV, Retention), A/B tests, funnels, cohort & segment analysis
Financial Analytics – P&L, Cash Flow, Budget vs Actual, Unit Economics, forecasting (Python / Power BI / Excel)
Data / BI Analytics – ETL pipelines, dashboards (Power BI, Metabase, Tableau, Looker Studio), automation, SQL optimization
Marketing / Sales Analytics – channel performance, ROMI/ROI, RFM, segmentation & sales forecasting
🔹 Stack & Tools
Python: pandas, numpy, matplotlib, seaborn, scikit-learn, Prophet, statsmodels
SQL: PostgreSQL, MSSQL, BigQuery, MySQL, SQLite — complex joins, window functions, CTEs, ad-hoc analytical queries
BI: Power BI, Tableau, Metabase, Google Looker Studio
Data: Excel, Google Sheets, PowerQuery, VBA, AppScript
Databases: MongoDB, PostgreSQL
Other: Git, Jupyter, REST API, JSON/XML, ETL automation tools
🔹 Achievements
– Automated financial and operational reporting across departments
– Built interactive Power BI & Metabase dashboards for management
– Designed SQL & MongoDB models for transaction analytics
– Created Python forecasting models improving accuracy by 20%
– Delivered insights that helped reduce operational costs by 15%
– Solved ad-hoc analytical tasks for finance, product, and operations teams (SQL + Power BI)
🔹 Languages & format
English — B1 | German — A2 | Russian — native | Ukrainian — native
Remote / full-time / B2B | Open to relocation
📎 CV: Notion CV + portfolio**
📱 TG: **@Yevhenii_successo
📄 Document (not supported)
#resume #cv #data #financial #middle #middle+ #dataanalyst #financialanalyst #bi #analyst #powerbi #sql #python #excel #googlesheets #metabase #mongodb #remote #relocation
Hi everyone
I’m Yevhenii Chernyshev — Data / BI / Financial Analyst with 15+ years in finance and 2 years in analytics.
I connect finance, business, and data to help teams make better decisions.
🔹 Core areas I cover
Business / Product Analytics – metrics (DAU/MAU, LTV, Retention), A/B tests, funnels, cohort & segment analysis
Financial Analytics – P&L, Cash Flow, Budget vs Actual, Unit Economics, forecasting (Python / Power BI / Excel)
Data / BI Analytics – ETL pipelines, dashboards (Power BI, Metabase, Tableau, Looker Studio), automation, SQL optimization
Marketing / Sales Analytics – channel performance, ROMI/ROI, RFM, segmentation & sales forecasting
🔹 Stack & Tools
Python: pandas, numpy, matplotlib, seaborn, scikit-learn, Prophet, statsmodels
SQL: PostgreSQL, MSSQL, BigQuery, MySQL, SQLite — complex joins, window functions, CTEs, ad-hoc analytical queries
BI: Power BI, Tableau, Metabase, Google Looker Studio
Data: Excel, Google Sheets, PowerQuery, VBA, AppScript
Databases: MongoDB, PostgreSQL
Other: Git, Jupyter, REST API, JSON/XML, ETL automation tools
🔹 Achievements
– Automated financial and operational reporting across departments
– Built interactive Power BI & Metabase dashboards for management
– Designed SQL & MongoDB models for transaction analytics
– Created Python forecasting models improving accuracy by 20%
– Delivered insights that helped reduce operational costs by 15%
– Solved ad-hoc analytical tasks for finance, product, and operations teams (SQL + Power BI)
🔹 Languages & format
English — B1 | German — A2 | Russian — native | Ukrainian — native
Remote / full-time / B2B | Open to relocation
📎 CV: Notion CV + portfolio**
📱 TG: **@Yevhenii_successo
Hi everyone
I’m Yevhenii Chernyshev — Data / BI / Financial Analyst with 15+ years in finance and 2 years in analytics.
I connect finance, business, and data to help teams make better decisions.
🔹 Core areas I cover
Business / Product Analytics – metrics (DAU/MAU, LTV, Retention), A/B tests, funnels, cohort & segment analysis
Financial Analytics – P&L, Cash Flow, Budget vs Actual, Unit Economics, forecasting (Python / Power BI / Excel)
Data / BI Analytics – ETL pipelines, dashboards (Power BI, Metabase, Tableau, Looker Studio), automation, SQL optimization
Marketing / Sales Analytics – channel performance, ROMI/ROI, RFM, segmentation & sales forecasting
🔹 Stack & Tools
Python: pandas, numpy, matplotlib, seaborn, scikit-learn, Prophet, statsmodels
SQL: PostgreSQL, MSSQL, BigQuery, MySQL, SQLite — complex joins, window functions, CTEs, ad-hoc analytical queries
BI: Power BI, Tableau, Metabase, Google Looker Studio
Data: Excel, Google Sheets, PowerQuery, VBA, AppScript
Databases: MongoDB, PostgreSQL
Other: Git, Jupyter, REST API, JSON/XML, ETL automation tools
🔹 Achievements
– Automated financial and operational reporting across departments
– Built interactive Power BI & Metabase dashboards for management
– Designed SQL & MongoDB models for transaction analytics
– Created Python forecasting models improving accuracy by 20%
– Delivered insights that helped reduce operational costs by 15%
– Solved ad-hoc analytical tasks for finance, product, and operations teams (SQL + Power BI)
🔹 Languages & format
English — B1 | German — A2 | Russian — native | Ukrainian — native
Remote / full-time / B2B | Open to relocation
📎 CV: Notion CV + portfolio**
📱 TG: **@Yevhenii_successo
📄 Document (not supported)
#resume #cv #data #financial #middle #middle+ #dataanalyst #financialanalyst #bi #analyst #powerbi #sql #python #excel #googlesheets #metabase #mongodb #remote #relocation
Hi everyone
I’m Yevhenii Chernyshev — Data / BI / Financial Analyst with 15+ years in finance and 2 years in analytics.
I connect finance, business, and data to help teams make better decisions.
🔹 Core areas I cover
Business / Product Analytics – metrics (DAU/MAU, LTV, Retention), A/B tests, funnels, cohort & segment analysis
Financial Analytics – P&L, Cash Flow, Budget vs Actual, Unit Economics, forecasting (Python / Power BI / Excel)
Data / BI Analytics – ETL pipelines, dashboards (Power BI, Metabase, Tableau, Looker Studio), automation, SQL optimization
Marketing / Sales Analytics – channel performance, ROMI/ROI, RFM, segmentation & sales forecasting
🔹 Stack & Tools
Python: pandas, numpy, matplotlib, seaborn, scikit-learn, Prophet, statsmodels
SQL: PostgreSQL, MSSQL, BigQuery, MySQL, SQLite — complex joins, window functions, CTEs, ad-hoc analytical queries
BI: Power BI, Tableau, Metabase, Google Looker Studio
Data: Excel, Google Sheets, PowerQuery, VBA, AppScript
Databases: MongoDB, PostgreSQL
Other: Git, Jupyter, REST API, JSON/XML, ETL automation tools
🔹 Achievements
– Automated financial and operational reporting across departments
– Built interactive Power BI & Metabase dashboards for management
– Designed SQL & MongoDB models for transaction analytics
– Created Python forecasting models improving accuracy by 20%
– Delivered insights that helped reduce operational costs by 15%
– Solved ad-hoc analytical tasks for finance, product, and operations teams (SQL + Power BI)
🔹 Languages & format
English — B1 | German — A2 | Russian — native | Ukrainian — native
Remote / full-time / B2B | Open to relocation
📎 CV: Notion CV + portfolio**
📱 TG: **@Yevhenii_successo
Hi everyone
I’m Yevhenii Chernyshev — Data / BI / Financial Analyst with 15+ years in finance and 2 years in analytics.
I connect finance, business, and data to help teams make better decisions.
🔹 Core areas I cover
Business / Product Analytics – metrics (DAU/MAU, LTV, Retention), A/B tests, funnels, cohort & segment analysis
Financial Analytics – P&L, Cash Flow, Budget vs Actual, Unit Economics, forecasting (Python / Power BI / Excel)
Data / BI Analytics – ETL pipelines, dashboards (Power BI, Metabase, Tableau, Looker Studio), automation, SQL optimization
Marketing / Sales Analytics – channel performance, ROMI/ROI, RFM, segmentation & sales forecasting
🔹 Stack & Tools
Python: pandas, numpy, matplotlib, seaborn, scikit-learn, Prophet, statsmodels
SQL: PostgreSQL, MSSQL, BigQuery, MySQL, SQLite — complex joins, window functions, CTEs, ad-hoc analytical queries
BI: Power BI, Tableau, Metabase, Google Looker Studio
Data: Excel, Google Sheets, PowerQuery, VBA, AppScript
Databases: MongoDB, PostgreSQL
Other: Git, Jupyter, REST API, JSON/XML, ETL automation tools
🔹 Achievements
– Automated financial and operational reporting across departments
– Built interactive Power BI & Metabase dashboards for management
– Designed SQL & MongoDB models for transaction analytics
– Created Python forecasting models improving accuracy by 20%
– Delivered insights that helped reduce operational costs by 15%
– Solved ad-hoc analytical tasks for finance, product, and operations teams (SQL + Power BI)
🔹 Languages & format
English — B1 | German — A2 | Russian — native | Ukrainian — native
Remote / full-time / B2B | Open to relocation
📎 CV: Notion CV + portfolio**
📱 TG: **@Yevhenii_successo
📄 Document (not supported)
#resume #cv #data #financial #middle #middle+ #dataanalyst #financialanalyst #bi #analyst #powerbi #sql #python #excel #googlesheets #metabase #mongodb #remote #relocation
Hi everyone
I’m Yevhenii Chernyshev — Data / BI / Financial Analyst with 15+ years in finance and 2 years in analytics.
I connect finance, business, and data to help teams make better decisions.
🔹 Core areas I cover
Business / Product Analytics – metrics (DAU/MAU, LTV, Retention), A/B tests, funnels, cohort & segment analysis
Financial Analytics – P&L, Cash Flow, Budget vs Actual, Unit Economics, forecasting (Python / Power BI / Excel)
Data / BI Analytics – ETL pipelines, dashboards (Power BI, Metabase, Tableau, Looker Studio), automation, SQL optimization
Marketing / Sales Analytics – channel performance, ROMI/ROI, RFM, segmentation & sales forecasting
🔹 Stack & Tools
Python: pandas, numpy, matplotlib, seaborn, scikit-learn, Prophet, statsmodels
SQL: PostgreSQL, MSSQL, BigQuery, MySQL, SQLite — complex joins, window functions, CTEs, ad-hoc analytical queries
BI: Power BI, Tableau, Metabase, Google Looker Studio
Data: Excel, Google Sheets, PowerQuery, VBA, AppScript
Databases: MongoDB, PostgreSQL
Other: Git, Jupyter, REST API, JSON/XML, ETL automation tools
🔹 Achievements
– Automated financial and operational reporting across departments
– Built interactive Power BI & Metabase dashboards for management
– Designed SQL & MongoDB models for transaction analytics
– Created Python forecasting models improving accuracy by 20%
– Delivered insights that helped reduce operational costs by 15%
– Solved ad-hoc analytical tasks for finance, product, and operations teams (SQL + Power BI)
🔹 Languages & format
English — B1 | German — A2 | Russian — native | Ukrainian — native
Remote / full-time / B2B | Open to relocation
📎 CV: Notion CV + portfolio**
📱 TG: **@Yevhenii_successo
Hi everyone
I’m Yevhenii Chernyshev — Data / BI / Financial Analyst with 15+ years in finance and 2 years in analytics.
I connect finance, business, and data to help teams make better decisions.
🔹 Core areas I cover
Business / Product Analytics – metrics (DAU/MAU, LTV, Retention), A/B tests, funnels, cohort & segment analysis
Financial Analytics – P&L, Cash Flow, Budget vs Actual, Unit Economics, forecasting (Python / Power BI / Excel)
Data / BI Analytics – ETL pipelines, dashboards (Power BI, Metabase, Tableau, Looker Studio), automation, SQL optimization
Marketing / Sales Analytics – channel performance, ROMI/ROI, RFM, segmentation & sales forecasting
🔹 Stack & Tools
Python: pandas, numpy, matplotlib, seaborn, scikit-learn, Prophet, statsmodels
SQL: PostgreSQL, MSSQL, BigQuery, MySQL, SQLite — complex joins, window functions, CTEs, ad-hoc analytical queries
BI: Power BI, Tableau, Metabase, Google Looker Studio
Data: Excel, Google Sheets, PowerQuery, VBA, AppScript
Databases: MongoDB, PostgreSQL
Other: Git, Jupyter, REST API, JSON/XML, ETL automation tools
🔹 Achievements
– Automated financial and operational reporting across departments
– Built interactive Power BI & Metabase dashboards for management
– Designed SQL & MongoDB models for transaction analytics
– Created Python forecasting models improving accuracy by 20%
– Delivered insights that helped reduce operational costs by 15%
– Solved ad-hoc analytical tasks for finance, product, and operations teams (SQL + Power BI)
🔹 Languages & format
English — B1 | German — A2 | Russian — native | Ukrainian — native
Remote / full-time / B2B | Open to relocation
📎 CV: Notion CV + portfolio**
📱 TG: **@Yevhenii_successo
📄 Document (not supported)
#resume #cv #data #financial #middle #middle+ #dataanalyst #financialanalyst #bi #analyst #powerbi #sql #python #excel #googlesheets #metabase #mongodb #remote #relocation
Hi everyone
I’m Yevhenii Chernyshev — Data / BI / Financial Analyst with 15+ years in finance and 2 years in analytics.
I connect finance, business, and data to help teams make better decisions.
🔹 Core areas I cover
Business / Product Analytics – metrics (DAU/MAU, LTV, Retention), A/B tests, funnels, cohort & segment analysis
Financial Analytics – P&L, Cash Flow, Budget vs Actual, Unit Economics, forecasting (Python / Power BI / Excel)
Data / BI Analytics – ETL pipelines, dashboards (Power BI, Metabase, Tableau, Looker Studio), automation, SQL optimization
Marketing / Sales Analytics – channel performance, ROMI/ROI, RFM, segmentation & sales forecasting
🔹 Stack & Tools
Python: pandas, numpy, matplotlib, seaborn, scikit-learn, Prophet, statsmodels
SQL: PostgreSQL, MSSQL, BigQuery, MySQL, SQLite — complex joins, window functions, CTEs, ad-hoc analytical queries
BI: Power BI, Tableau, Metabase, Google Looker Studio
Data: Excel, Google Sheets, PowerQuery, VBA, AppScript
Databases: MongoDB, PostgreSQL
Other: Git, Jupyter, REST API, JSON/XML, ETL automation tools
🔹 Achievements
– Automated financial and operational reporting across departments
– Built interactive Power BI & Metabase dashboards for management
– Designed SQL & MongoDB models for transaction analytics
– Created Python forecasting models improving accuracy by 20%
– Delivered insights that helped reduce operational costs by 15%
– Solved ad-hoc analytical tasks for finance, product, and operations teams (SQL + Power BI)
🔹 Languages & format
English — B1 | German — A2 | Russian — native | Ukrainian — native
Remote / full-time / B2B | Open to relocation
📎 CV: Notion CV + portfolio**
📱 TG: **@Yevhenii_successo
Hi everyone
I’m Yevhenii Chernyshev — Data / BI / Financial Analyst with 15+ years in finance and 2 years in analytics.
I connect finance, business, and data to help teams make better decisions.
🔹 Core areas I cover
Business / Product Analytics – metrics (DAU/MAU, LTV, Retention), A/B tests, funnels, cohort & segment analysis
Financial Analytics – P&L, Cash Flow, Budget vs Actual, Unit Economics, forecasting (Python / Power BI / Excel)
Data / BI Analytics – ETL pipelines, dashboards (Power BI, Metabase, Tableau, Looker Studio), automation, SQL optimization
Marketing / Sales Analytics – channel performance, ROMI/ROI, RFM, segmentation & sales forecasting
🔹 Stack & Tools
Python: pandas, numpy, matplotlib, seaborn, scikit-learn, Prophet, statsmodels
SQL: PostgreSQL, MSSQL, BigQuery, MySQL, SQLite — complex joins, window functions, CTEs, ad-hoc analytical queries
BI: Power BI, Tableau, Metabase, Google Looker Studio
Data: Excel, Google Sheets, PowerQuery, VBA, AppScript
Databases: MongoDB, PostgreSQL
Other: Git, Jupyter, REST API, JSON/XML, ETL automation tools
🔹 Achievements
– Automated financial and operational reporting across departments
– Built interactive Power BI & Metabase dashboards for management
– Designed SQL & MongoDB models for transaction analytics
– Created Python forecasting models improving accuracy by 20%
– Delivered insights that helped reduce operational costs by 15%
– Solved ad-hoc analytical tasks for finance, product, and operations teams (SQL + Power BI)
🔹 Languages & format
English — B1 | German — A2 | Russian — native | Ukrainian — native
Remote / full-time / B2B | Open to relocation
📎 CV: Notion CV + portfolio**
📱 TG: **@Yevhenii_successo
📄 Document (not supported)
#resume #cv #data #financial #middle #middle+ #dataanalyst #financialanalyst #bi #analyst #powerbi #sql #python #excel #googlesheets #metabase #mongodb #remote #relocation
Hi everyone
I’m Yevhenii Chernyshev — Data / BI / Financial Analyst with 15+ years in finance and 2 years in analytics.
I connect finance, business, and data to help teams make better decisions.
🔹 Core areas I cover
Business / Product Analytics – metrics (DAU/MAU, LTV, Retention), A/B tests, funnels, cohort & segment analysis
Financial Analytics – P&L, Cash Flow, Budget vs Actual, Unit Economics, forecasting (Python / Power BI / Excel)
Data / BI Analytics – ETL pipelines, dashboards (Power BI, Metabase, Tableau, Looker Studio), automation, SQL optimization
Marketing / Sales Analytics – channel performance, ROMI/ROI, RFM, segmentation & sales forecasting
🔹 Stack & Tools
Python: pandas, numpy, matplotlib, seaborn, scikit-learn, Prophet, statsmodels
SQL: PostgreSQL, MSSQL, BigQuery, MySQL, SQLite — complex joins, window functions, CTEs, ad-hoc analytical queries
BI: Power BI, Tableau, Metabase, Google Looker Studio
Data: Excel, Google Sheets, PowerQuery, VBA, AppScript
Databases: MongoDB, PostgreSQL
Other: Git, Jupyter, REST API, JSON/XML, ETL automation tools
🔹 Achievements
– Automated financial and operational reporting across departments
– Built interactive Power BI & Metabase dashboards for management
– Designed SQL & MongoDB models for transaction analytics
– Created Python forecasting models improving accuracy by 20%
– Delivered insights that helped reduce operational costs by 15%
– Solved ad-hoc analytical tasks for finance, product, and operations teams (SQL + Power BI)
🔹 Languages & format
English — B1 | German — A2 | Russian — native | Ukrainian — native
Remote / full-time / B2B | Open to relocation
📎 CV: Notion CV + portfolio**
📱 TG: **@Yevhenii_successo
Hi everyone
I’m Yevhenii Chernyshev — Data / BI / Financial Analyst with 15+ years in finance and 2 years in analytics.
I connect finance, business, and data to help teams make better decisions.
🔹 Core areas I cover
Business / Product Analytics – metrics (DAU/MAU, LTV, Retention), A/B tests, funnels, cohort & segment analysis
Financial Analytics – P&L, Cash Flow, Budget vs Actual, Unit Economics, forecasting (Python / Power BI / Excel)
Data / BI Analytics – ETL pipelines, dashboards (Power BI, Metabase, Tableau, Looker Studio), automation, SQL optimization
Marketing / Sales Analytics – channel performance, ROMI/ROI, RFM, segmentation & sales forecasting
🔹 Stack & Tools
Python: pandas, numpy, matplotlib, seaborn, scikit-learn, Prophet, statsmodels
SQL: PostgreSQL, MSSQL, BigQuery, MySQL, SQLite — complex joins, window functions, CTEs, ad-hoc analytical queries
BI: Power BI, Tableau, Metabase, Google Looker Studio
Data: Excel, Google Sheets, PowerQuery, VBA, AppScript
Databases: MongoDB, PostgreSQL
Other: Git, Jupyter, REST API, JSON/XML, ETL automation tools
🔹 Achievements
– Automated financial and operational reporting across departments
– Built interactive Power BI & Metabase dashboards for management
– Designed SQL & MongoDB models for transaction analytics
– Created Python forecasting models improving accuracy by 20%
– Delivered insights that helped reduce operational costs by 15%
– Solved ad-hoc analytical tasks for finance, product, and operations teams (SQL + Power BI)
🔹 Languages & format
English — B1 | German — A2 | Russian — native | Ukrainian — native
Remote / full-time / B2B | Open to relocation
📎 CV: Notion CV + portfolio**
📱 TG: **@Yevhenii_successo