Credit Risk Data Engineer
New
K
KiwiCredit risk
100% remote — [Argentina, Colombia, Dominican Republic].Full-TimeMiddle
Salary not disclosed
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Job Details
- Languages
- Bilingual in Spanish and English.
- Experience
- 4+ years of experience in data engineering or analytics engineering, including time as the main owner of a production data platform.
- Required Skills
- PythonSQLSnowflakeAirflowData modelingdbt
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; Snowflake is preferred.
- Experience with a transformation framework such as dbt, using version control, code review, and CI.
- Python skills for pipelines, tests, and automation.
- Experience building data quality tests and alerting with dbt tests, Great Expectations, Monte Carlo, Elementary, or custom checks.
- Experience with an orchestration tool such as Airflow, Dagster, or Prefect.
- Ability to write data contracts, incident notes, or table documentation for others to rely on.
- Nice to have: fintech or lending experience, especially with credit bureau or bank data.
- Nice to have: experience supporting data scientists with feature tables and training datasets, including avoiding leakage.
- Nice to have: familiarity with model monitoring concepts such as PSI, drift, and fill rates; exposure to lending compliance; or experience with Metabase.
Responsibilities
- Own the inventory of credit data sources, including vendor, app, and loan-servicing data.
- Define the data required from each source, its grain and freshness, and where it is stored.
- Write data contracts with engineers building vendor integrations.
- Confirm raw vendor responses are stored completely and reusable for model development and audits.
- Monitor source and model-input freshness, volume, fill rate, schema, and drift; set up alerts for deviations.
- Triage data incidents, identify root causes, route fixes to the right owners, and track them to closure.
- Own the Credit Risk layer of the Snowflake warehouse and build and maintain its pipelines and transformations.
- Keep modeling datasets reproducible and support point-in-time training sets without leakage.
- Maintain Credit Risk dashboard tables, governed metrics, automated data tests, documentation, and lineage.
- Maintain the owner registry and support vendor oversight by checking SLAs and reconciling pull counts against invoices.
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