Oleg Kotenko

Orchestration evaluation: Compared PoCs with Prefect, Airflow and Kestra; basis for selecting Airflow. SSIS → Airflow migration: Migrated SSIS packages (ETL loaders, cross-DB transfers, tabular refreshes) to Airflow DAGs; platform evolved with DevOps. Heterogeneous databases: SQL Server, Oracle and PostgreSQL across multiple data layers. Tabular models: Built/extended SSAS Tabular models via Tabular Editor, Power BI and DAX Studio. Azure DevOps: Git, Boards (user stories, tasks) and Wiki.

MII data integration: Co-built a pipeline delivering FHIR resources to the German Medical Informatics Initiative. Airflow orchestration: Workflows incl. HL7-to-FHIR parsing; extraction from clinical systems and Oracle via Mirth Connect. Metadata DB: Designed the PostgreSQL structure (staging tables, transformation logic, schemas). Validation: Python-based data checks. Research support: Custom data extracts for research groups. Cross-site collaboration: Regular exchange with German university hospitals.

ETL pipelines: Daily processing of files with 90M+ records, including storage and efficient data structures. Predictive models: Pipelines supporting forecasting models; GPU-based computation to speed up analysis. SSIS → Prefect migration: Improved flexibility, maintainability and workflow orchestration.Performance: Faster processing through asynchronous tasks and parallel processing.

ETL pipelines: Built efficient extraction processes from SQL databases, APIs, CSV files, AWS and Azure; improved data quality and integrity. Standardized data flows: Unified ETL processes across all company projects for better scalability and faster delivery of new projects.

Pricing data ETL: Automated jobs integrating and segmenting price and competitor data; streamlined and accelerated the work of price analysts. Promo planning automation: End-to-end workflow including validation and cross-department interfaces; replaced a manual process. Reporting: Automated price and margin reports; consistent KPIs and reliable data for management decisions.

Reporting optimization: Scalable reporting models for budget planning, sales steering and forecasting in the professional customer segment. Forecast models: Data-driven forecasts for store openings, customer development, and assortment and pricing decisions.

Analytical tools: Implemented tools to assess marketing and trade marketing effectiveness. KPIs: Defined and monitored key performance indicators, making performance measurable. Data visualization: Dashboards and reports built with Excel, VBA, Tableau and Python.

Sales analytics: Provided analytical reports on sales, budgets and forecasts, plus ad-hoc analyses for the key account manager.