Head of Data Engineering

5+ years of experience designing and operating production data pipelines at scale, ideally within fintech or lending environments B2 English level or higher Demonstrated ability to produce a technical diagnostic and roadmap within the first weeks of a role and present it credibly to executive stakeholders Proven experience implementing medallion architecture with explicit schema contracts and lineage between layers Strong proficiency in Google Cloud (BigQuery) and Azure (Data Factory, ADLS) Track record of hiring and leading data engineering teams of 2 to 5 engineers, including standing up intake processes Production-level Python and strong SQL fluency, including BigQuery-specific optimization patterns like partitioning, clustering, and cost control Comfortable operating in a hands-on capacity alongside the team Skills Data Engineering Python SQL Google Cloud BigQuery Azure Azure Data Factory ADLS Medallion Architecture Data Governance Lead the data engineering function build-out from zero, owning all pipelines and establishing freshness monitoring across Azure, GCP, and third-party integrations. Implement a medallion architecture consisting of bronze, silver, and gold layers to support feature stores and agent-ready data planes. Build a comprehensive data catalog and dictionary with canonical join keys and a full applicant data map. Deliver an architectural diagnostic and sequenced build roadmap within the first 30 days to executive stakeholders. Stand up intake and request-management processes for cross-functional stakeholders including Underwriting, Collections, and Product. Implement audit trails, identity governance, and cost monitoring across BigQuery and Azure platforms. Evaluate and drive the cloud consolidation strategy, proposing and executing the transition path toward Azure and Databricks. Hire, mentor, and lead the data engineering team as the platform scales.