Data Engineer

## Responsibilities - 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. ## Requirements - 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). ## 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 ## Benefits - Opportunity to work on mission-critical data products. - Collaborate with cross-functional teams including Credit Portfolio Management, Policy, Modeling, and Finance. - Work on AI/agent patterns and data contracts for real-time decisioning and underwriting optimization.