Data Engineer

What you’ll do Own the global UW tables (canonical facts/dimensions for applications, decisions, features, repayments, delinquency) with clear SLAs for freshness, completeness, accuracy, and data lineage. Design for AI-agents and humans: consistent IDs, canonical events, explicit metric definitions, rich metadata (schemas, data dictionaries), and machine-readable data contracts. Build & run pipelines (batch + streaming) that feed UW scoring, real-time decisioning, monitoring, and underwriting optimization. Instrument quality & observability (alerts, audits, reconciliation, backfills) and drive incident/root-cause reviews. Partner closely with Credit Portfolio Management, Policy teams, Modeling teams, and treasury and finance teams to land features for RUE and consumer-centric models, plus regulatory and management reporting.

Tech stack (what we use) Languages: SQL, PySpark, Python Frameworks: Apache Airflow, AWS Glue, Kafka, Redshift Cloud & DevOps: AWS (S3, Lambda, CloudWatch, SNS/SQS, Kinesis), Terraform; Git; CI/CD

What you’ll bring Proven ownership of mission-critical data products (batch + streaming). Data modeling, schema evolution, data contracts, and strong observability chops. Familiarity with AI/agent patterns (agent-friendly schemas/endpoints, embeddings/vector search).