Lead Data Engineer
RemoteUnited States
Job Summary
Design the lakehouse using Apache Iceberg on object storage with dbt models and a query engine. Build secure, least-privilege ingestion from bank systems via log-based CDC, query-based methods, batch, or SFTP fallbacks. Own data modeling for the semantic and metric layer covering deposits, concentration, uninsured exposure, and asset quality. Handle schema drift, data quality, and reconciliation to make ingestion observable and recoverable. Partner with AI/ML teams on the structured-query path and with Security on PII classification, adhering to regulatory data-handling requirements. Document data lineage, transformation logic, and access controls for audit and exam readiness. Define and enforce data contracts, quality thresholds, and alerting for pipeline failures. Report to the SVP of Technology and Commercial AI with on-call rotation covering data pipeline reliability.
Required Qualifications
- 8–12+ years in data engineering with end-to-end ownership of ingestion through serving, and 2+ years in a lead or senior role
- Strong Python and expert SQL; rigorous data modeling for analytics
- Hands-on lakehouse experience (Iceberg/Delta/Hudi or equivalent) and modern transformation tooling
- Built reliable pipelines from messy operational and transactional source systems
- Comfort with CDC mechanics and the realities of pulling from databases you do not control
- Bachelor's degree in computer science, mathematics, information systems, or a related field, or equivalent hands-on experience
- Up to 20% travel time may be required
Desired Qualifications
- Experience with financial or core-banking data, or FFIEC / Call Report data specifically
- Strong SQL Server familiarity
- Data contracts, lineage, and governance practices
- Experience in the financial services industry or a regulated data environment strongly preferred
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