Credit Risk Data Engineer

Job Description

REQUIREMENTS

• 4+ years of experience in data engineering or analytics engineering, including time as the main owner of a production data platform.
• Expert SQL and strong data modeling skills, including dimensional models, slowly changing data, and point-in-time snapshots.
• Hands-on experience with a cloud data warehouse, ideally Snowflake.
• Experience with a transformation framework such as dbt, using version control, code review, and CI.
• Python for pipelines, tests, and automation.
• A track record of building data quality tests and alerting, using dbt tests, Great Expectations, Monte Carlo, Elementary, or custom checks.
• Experience with an orchestration tool such as Airflow, Dagster, or Prefect.
• An ownership mindset: you notice problems before others do, follow them to the root cause, and close them out.
• Clear written communication: you can write a data contract, an incident note, or table documentation that others rely on.

RESPONSIBILITES

Data availability

• Own the inventory of every credit data source, including TransUnion, Clarity, Plaid, Prism, and internal data from the app and loan servicing.
• Define, for each source, what data Credit Risk needs, at what grain, how fresh it must be, and where it lands.
• Write data contracts with the engineers who build the vendor integrations, covering expected fields, types, null rules, and volumes.
• Confirm that raw vendor responses are stored completely and can be reused for model development and audits.

Monitoring and alerting

• Monitor freshness, volume, fill rate, and schema for every source and every model input.
• Set up alerts for when a feature’s fill rate drops, a vendor stops returning a field, or volumes deviate from baseline.
• Track input drift for production models alongside the data scientists.
• Triage data incidents: find the root cause, route the fix to the right owner, and track it to closure.

Data warehouse

• Own the Credit Risk layer of our Snowflake warehouse, including raw, staging, mart, and feature tables.
• Build and maintain the pipelines and transformations behind application, loan, performance, and vendor data.
• Keep modeling datasets reproducible, so data scientists can rebuild a point-in-time training set without leakage.
• Maintain the tables behind the Credit Risk dashboards and governed metrics.

Data quality and governance

• Write automated tests for keys, duplicates, ranges, referential integrity, and reconciliation between sources.
• Maintain documentation and lineage: what each table and field means, where it comes from, and who uses it.
• Keep the owner registry up to date for every vendor, model, and model input.
• Support vendor oversight by checking SLAs and reconciling pull counts against vendor invoices.

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