Core Expertise:

Data Engineer with 8+ years of experience in data management, specializing in designing DWH architectures from scratch, building fault-tolerant ETL/ELT pipelines, and integrating heterogeneous data sources. Focused on batch and stream processing for high-load systems (2+ TB / 10M+ daily events) using Python, Apache Airflow, PostgreSQL, and ClickHouse.

Combining:

• DWH & Data Architecture: ER diagram modeling, Staging/ODS/Core layer design, and analytical data mart engineering for BI and ML.
• Pipeline Engineering & Orchestration: Airflow DAG development, CDC (Change Data Capture) implementation, pipeline idempotency, and load consistency.
• Data Governance & Reliability: Automated Data Quality and Freshness monitoring, anomaly alerting, and query performance optimization.

Operating Across Three Levels:

1. Ingestion & Infrastructure: Raw log ingestion (APIs, CDC, flat files), stream validation, deduplication, and reliable loading into DWH.
2. Modeling & Transformation: Optimizing data marts (dbt, SQL, Materialized Views), configuring incremental refreshes (pg_cron, dblink), and reducing processing time by 3–4x.
3. Business Value: Delivering dependable data infrastructure powering executive BI dashboards (Metabase/Tableau), feature stores, and end-to-end analytics systems.

Data Engineer

Location: Novosibirsk, Russia (UTC+7)

Email: v.tischenko@inbox.ru

Telegram: @vit_v_t

Scope of Responsibility:
End-to-end data management bridging Data Engineering and Data Analytics. Spanning DWH architecture design, ETL/ELT pipeline construction, and Data Quality assurance to unit economics modeling (CAC/LTV) and executive delivery of product/marketing reporting.

Key Responsibilities:

Infrastructure, Data Pipelines (ETL/ELT) & Data Quality:

• Architected and built the data ingestion ecosystem from scratch: Configured regular incremental extraction of raw events and transactions from third-party APIs (Meta Marketing API, Yandex Metrica Logs API, Google Analytics, etc.) and relational sources into a centralized PostgreSQL DWH.
• Executed a large-scale engineering project to build affiliate program infrastructure: Launched ETL processes, implemented automated data enrichment, designed analytical data marts, and delivered target metrics to executive dashboards.
• Maintained full end-to-end data ownership across all pipeline stages (from initial raw source and Staging layers to final data marts and business reporting).
• Developed and automated Apache Airflow DAGs (utilizing HttpHook, custom operators, and dependency management) to ensure fault-tolerant data loading and deduplication.
• Implemented Data Quality monitoring and reconciliation workflows: Created Metabase alert dashboards to monitor ingestion completeness, track data freshness, and immediately detect operational anomalies.

DWH Modeling & Database Engineering:

• Designed ER data models, Staging layers, and Data Marts optimized for business analytical queries.
• Optimized complex SQL queries (including CTEs, window functions, and LATERAL JOINs), configured indexing strategies, and automated materialized view refreshes via pg_cron and dblink, significantly reducing data aggregation times.
• Engineered data structures and SQL logic for promo campaign attribution (evaluating discount halo-effects on baseline sales) and analyzing non-trivial user journey pathways.

Data Analytics & Unit Economics:

• Delivered a major analytics initiative consolidating key marketing metrics (CAC and LTV) broken down by channels and cohort segments.
• Conducted comprehensive product unit economics calculations to evaluate business model viability and support executive pricing decisions.
• Built an end-to-end analytics framework and metric system (covering AOV, Rolling AOV, Retention, Churn, and onboarding funnels).
• Developed interactive dashboards in Metabase and Tableau (modeling affiliate sales performance, ad channel efficiency, and conversion metrics).
• Analyzed user experience sequences using graph analysis (Python: NetworkX, Plotly) to identify bottlenecks and friction points in customer pathways.

Key Achievements:

• Infrastructure & Data Quality: Designed an end-to-end ETL infrastructure from scratch (including a dedicated affiliate program module) using Apache Airflow and deployed a Data Quality monitoring framework in Metabase, securing complete governance over data accuracy and pipeline reliability.
• Analytics & Business Impact: Delivered cross-functional projects calculating CAC/LTV and unit economics, alongside an executive BI dashboard system in Tableau/Metabase, establishing full ROI transparency across marketing channels and accurately quantifying the halo-effects of discount campaigns.

Data Analyst / Data Engineer

September 2025 — September 2026

(1 year and 1 month)

Scope of Responsibility:
Building and maintaining end-to-end data infrastructure (Data Engineering), testing product hypotheses (A/B testing), and delivering advanced analytics (Data Science / ML & Data Analytics): spanning source integrations, ETL/ELT pipelines, DWH architecture, ML modeling, and experimental design.

System Scale & Data Volume:

• DWH Capacity: Managing a Data Warehouse containing 2+ TB of raw and aggregated data (ClickHouse, PostgreSQL).
• Data Flow: Ingesting and validating high-throughput streams of up to 5–10 million daily events (raw web/mobile analytics logs, transactional data, CRM event triggers).
• Pipelines (ETL/ELT): Orchestrating and maintaining 30+ production Airflow DAGs/pipelines with automated SLA, latency, and Data Quality/Freshness checks.

Key Responsibilities:

Data Engineering, ETL/ELT & DWH Architecture:

• Architected and deployed a 2+ TB Data Warehouse ecosystem: Structured Staging, ODS, and Core (Marts) layers to consolidate data from marketing platforms, CRMs, and web analytics tools.
• Automated batch and incremental ETL/ELT data processing pipelines (Python, SQL) handling up to 10M daily events with built-in deduplication and ingestion validation.
• Engineered and optimized complex SQL scripts, stored procedures, and Materialized Views (dbt, pg_cron) to accelerate data mart aggregation by 3–4x and prepare feature stores for ML tasks.
• Configured automated data freshness and accuracy test suites (Data Quality) to validate data prior to downstream reporting and ML consumption.

Data Science, Machine Learning & A/B Testing:

• A/B Testing: Designed and executed end-to-end A/B experiments (sample size determination, experiment design, MDE, p-value statistical significance testing), building event logging infrastructure to ensure proper traffic splitting.
• ML & Data Science: Developed and deployed machine learning models (Python, scikit-learn, statsmodels) addressing core business cases (churn forecasting, LTV prediction, customer segmentation/classification, sales forecasting).
• Built automated feature stores, scoring pipelines, and feedback loops back into DWH and CRM systems.

Data Analytics & End-to-End BI Reporting:

• Engineered end-to-end attribution models evaluating marketing channel performance, ROMI, CAC, and LTV.
• Designed and delivered executive BI dashboards tracking sales funnels, user retention, and core operational KPIs for leadership and product teams.
• Bridged communication between business stakeholders and technical teams, authoring technical specs for event logging and tracking enhancements.

Key Achievements:

• Scalable Infrastructure & ML: Built an automated DWH ingestion and consolidation pipeline from scratch handling 10M daily events and establishing reliable ML data prep and A/B testing pipelines.
• Performance Optimization: Migrated core transformations to incremental dbt models in ClickHouse, reducing daily data mart processing time from 15–20 minutes down to just a few minutes.
• Analytics & Business Impact: Established a single source of truth for analytics and BI dashboards, providing transparent tracking of critical metrics (CAC, LTV, funnels) and enabling data-driven decision-making through statistically validated hypotheses.

Altair LLC

Data Engineer / Data Analyst

October 2021 — September 2025

(4 years)

Scope of Responsibility:
Data consolidation and preparation (Data Engineering), PostgreSQL/MySQL database maintenance, and providing company-wide end-to-end analytics, reporting, and BI dashboards (Data Analytics).

Key Responsibilities:

Database Engineering & Automation:

• Designed and maintained table schemas and data marts within DBMS environments (PostgreSQL / MySQL) to support recurring reporting workflows.
• Authored and optimized SQL queries (complex aggregations, joins, window functions) to ensure fast analytics data retrieval.
• Automated data extraction and assembly from diverse external systems and services into a unified database, reducing manual data processing to a minimum.
• Validated incoming data completeness and integrity prior to ingestion into executive reports and data marts.

Data Analytics & Business Reporting:

• Built and maintained interactive BI dashboards visualizing core business KPIs and sales trends.
• Developed advanced analytical models in Excel / Google Sheets (complex formulas, pivot tables) for operational performance tracking.
• Conducted analytical deep-dives, cohort analysis, and sales/marketing funnel performance evaluations.
• Prepared recurring and ad-hoc data extracts supporting executive leadership and cross-functional teams.

Key Achievements:

• Automation & Efficiency: Transitioned core business reporting from manual spreadsheets to automated SQL data marts and BI dashboards, significantly cutting down report generation cycles.
• Analytics & Transparency: Built a streamlined KPI monitoring system using PostgreSQL and BI tools, delivering reliable, real-time analytics to executive stakeholders.

ATR LLC

Project Manager
Data & Marketing Analyst

June 2018 — October 2021

(3 years and 5 months)

Tomsk Institute of Economics and Law (2003)

Advanced data analytics
Karpov.courses
2023

"Data Analyst" Simulator
Simulative
2022

Data Engineering
Karpov.courses
2021

SQL for Oracle Certification
Udemy
2018

Pandas

Data Visualization

Machine Learning

Time Series

Feature Engineering

Machine Learning Explainability

Computer Vision

Deep Learning

Geospatial Analysis