Katja Ilicheva

Achievements: - Built a validation suite with 200+ checks, improving UA pipeline reliability. - Identified rare source behavior causing data drift (annual store dumps) and created reusable RCA queries. - Found duplicated transactions in Databricks, preventing incorrect financial/performance reporting. - Reconciled mismatches between internal systems and external store data (Apple, Google, Amazon) and improved daily anomaly detection in installs and revenue using Z-score methods.

Achievements: - Developed and deployed an anomaly-detection workflow that reduced support reaction time by 60–80%. - Built a pipeline to detect subtle data stoppages across providers, enabling proactive incident handling. - Implemented around 20 automated DQ alerts, improving early detection of ingestion and transformation defects. -Delivered dashboards and ad hoc analyses that provided actionable insights for cross-functional teams.

- Designed a data model for SAP Business Warehouse supporting metadata harvesting. - Developed SQL/Python scripts to collect metadata from SAP HANA, PostgreSQL and ClickHouse, improving data consistency. - Built data marts for a shelf-space optimization project, contributing to an expected 3.4% revenue increase. - Improved DWH reliability by implementing data quality validation across ingestion and transformation. - Defined technical specs for ETL developers, ensuring accurate pipeline implementation.

Held roles in demand analysis, planning and business support in multinational, distributed teams, strengthening collaboration, process optimization and decision-making across markets. At Storch-Ciret Group, a German manufacturing company, began transitioning into analytics: completed an MIT analytics course, introduced process optimizations and early data management practices, and collaborated weekly with the German Logistics (Demand Planning) team; completed three training visits to Germany.