getSummary
Written in English from the original postingOwns global underwriting canonical tables and data contracts with SLAs for freshness, completeness, accuracy, and lineage. Builds and operates batch and streaming pipelines that support scoring, real-time decisioning, monitoring, and reporting. Uses SQL, Python, PySpark, Airflow, AWS Glue, Kafka, Redshift, AWS services, Terraform, Git, and CI/CD.
Job description
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).