Required Qualifications :
Strongly Preferred Qualifications :
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- Data Engineering : Proven track record of architectural ownership and end-to-end system design.
- Production Databricks Depth : Extensive experience with Delta Lake, Spark (PySpark & Spark SQL), Unity Catalog, job orchestration, and performance tuning (partitioning, clustering, Z-ordering, file compaction).
- Financial Time-Series Domain Expertise : Strong understanding of structured finance instruments (ABS/MBS, loan-level data, cash flow analytics) and time-series correctness (bitemporal processing, point-in-time alignment, late-arriving data).
- Legacy Code Discovery : Demonstrated ability to read unfamiliar, monolithic production codebases (e.g., legacy SQL/stored procedures, C++, Java, or custom scripts) to recover underlying business logic without relying on specs.
- Demonstrated Impact : Applicants must be able to quantify the scale and business outcomes of their past initiatives (e.g., performance gains, cost reductions, data parity metrics, scale handled).
- Languages & Tooling : Advanced Python, SQL, shell scripting, and CI/CD-driven infrastructure delivery (e.g., Git, Terraform).
Strongly Preferred Qualifications :
- Conference Presentation or Published Work: Prior speaking engagement at major industry events (e.g., Databricks Data + AI Summit, AWS re:Invent, Snowflake Summit, FINOS, or regional Data Engineering conferences) OR published technical blogs/whitepapers on complex data engineering or financial modeling.
- Legacy Migration Cutover Experience: Proven history of taking a legacy platform completely off-line through rigorous parity testing and controlled cutover.
- Governance & Cost Control: Hands-on execution with Delta Live Tables, Unity Catalog governance, and FinOps/cost management.
For applications and inquiries, contact:[email protected]
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