Senior Data Engineer - WOXA GROUP

• Design and build a point-in-time research data lake that records the state of market data as it was known at each historical moment, eliminating look-ahead bias in research and backtesting.

Reproducible Feature Engineering Pipelines

• Build version-controlled, reproducible feature engineering pipelines. Every model output must be traceable back to the data state and transformations that produced it.

• Outliers and anomalous prints
• Corporate actions (splits, dividends, mergers, spin-offs)
• Symbol mapping and ticker changes across vendors

• Tune the data retrieval layer for both research and production access patterns — columnar formats, partitioning, caching, and query performance tuning.

Monitor production data flow and quality end-to-end:

• Real-time data flow from exchanges
• Data availability checks for model predictions
• Continuous data quality monitoring in production

Education:

• Bachelor's degree or higher in Computer Science, Engineering, Mathematics, or a related quantitative field.

Experience:

• Minimum 5 years in Data Engineering, with at least 2 years on production AWS infrastructure.
• Track record of designing data systems from greenfield through to production.
• Strong computer-science fundamentals (data structures, algorithms, distributed systems).
• Able to manage multiple parallel projects independently with strong ownership.

Must-Have Skills:

• Production-grade Python (not notebook-only).
• AWS production experience: S3, Glue, Athena or EMR, Lake Formation, Lambda.
• Workflow orchestration: Airflow, Dagster, or Prefect.
• Time-series & columnar storage: Parquet, Arrow, ClickHouse, or equivalent.
• Data lake house: Delta Lake, Iceberg, or Hudi.
• Advanced SQL — both analytical and transactional databases.
• Database performance: indexing, partitioning, query optimization.
• Containerization & IaC: Docker, Terraform (or equivalent).
• Data architecture — designing systems that scale from day

Nice-to-Have:

• Real-time streaming: Kafka, AWS Kinesis, or MSK - comfortable either building or operating.
• Equity market data experience — has worked with messy financial data (tick data, OHLC, corporate actions, vendor reconciliation).
• Vendor integration — onboarding and reconciling data from multiple market-data vendors.
• kdb+ or other specialized time-series database experience.
• Experience inside a systematic trading team or hedge fund.
• ML lifecycle support — feature stores, model registry, experiment tracking.