Beskrivning
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).
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).
