Wiktor Nejman

— Payments & Risk Data Platform

• • Own and evolve the payments data platform serving risk, compliance, and product analytics teams, building real-time streaming pipelines with Spark Structured Streaming and Apache Kafka for paymeant event processing, fraud detection signals, and settlement reporting

• • Designed and implemented Lakehouse architecture with Delta Lake on Databricks (AWS and Azure), migrating legacy Redshift pipelines to Medallion Architecture (bronze/silver/gold) with significant query performance and cost improvements

• • Implemented data governance layer with Unity Catalog: data lineage tracking, metadata management, schema evolution, column-level access controls, and audit trails for regulatory compliance; maintained data catalog enabling cross-team data discovery

• • Developed automated data quality frameworks using Great Expectations, establishing data contracts between upstream services and downstream consumers with SLA monitoring and alerting

• • Drove platform cost optimization through partitioning strategies, Z-ordering, Photon engine tuning, and tiered storage policies, materially reducing compute and storage spend

• • Standardized CI/CD for data pipelines using Databricks Asset Bundles and GitOps workflows; mentor data engineers and lead architecture reviews across the team

Search & Personalization Data Engineering

• • Built and maintained scalable data pipelines for the search ranking and personalization team using Apache Spark (PySpark) and Databricks, processing large-scale clickstream and product interaction data

• • Designed event-driven data architecture with Apache Kafka for real-time ingestion of search events, user session data, and product interaction signals into the analytics platform

• • Implemented Lakehouse architecture with Delta Lake, including partitioning, compaction, schema evolution, and data versioning to ensure backward compatibility across downstream workloads

• • Designed dimensional data models and star schema structures using dbt and SQL for the analytics data warehouse, enabling self-service reporting for product, merchandising, and pricing teams

• • Built data quality validation framework with Great Expectations and automated anomaly detection, reducing data incidents and improving pipeline SLA compliance

• • Orchestrated complex data workflows with Apache Airflow, building multi-step DAGs with dependency management, monitoring, and automated alerting

• • Delivered data engineering solutions for fintech and banking clients across multiple concurrent engagements, building ETL pipelines for KYC/AML compliance, regulatory reporting, and customer analytics

• • Built data pipelines on Snowflake and AWS Redshift using Python and PySpark, implementing scalable ingestion and transformation workflows with Airflow orchestration across diverse client data sources

• • Developed cloud-native ETL workflows on AWS (S3, Glue, Lambda) and Azure (Data Factory, ADLS Gen2) depending on client infrastructure, ensuring cross-platform data delivery and SLA compliance

• • Designed data warehouse schemas using dimensional modeling and star schema patterns for client analytics platforms, enabling self-service BI reporting across financial services engagements

• • Implemented pipeline health monitoring and alerting using CloudWatch and custom Python scripts, ensuring reliable data delivery across concurrent client projects

• • Established data engineering best practices across the team: introduced testing standards with pytest, code review workflows, and data validation frameworks