Credit Risk Data Engineer

This is a fully remote position, open to applicants in Dominican Republic, +2 more countries.

📋 Description

• Take charge of the inventory for all credit data sources, which include TransUnion, Clarity, Plaid, Prism, and internal data from the app and loan servicing.

• Establish the necessary data requirements, granularity, freshness, and destination for each source.

• Create data contracts with engineers responsible for developing vendor integrations.

• Ensure that raw vendor responses are fully stored for model development and audits.

• Oversee the freshness, volume, fill rate, and schema for each source and model input.

• Set up alerts for any drops in feature fill-rate, missing vendor fields, and deviations in volume.

• Monitor input drift for production models in collaboration with data scientists.

• Manage data incidents by identifying root causes, directing fixes, and tracking their resolution.

• Own the Credit Risk layer within the Snowflake warehouse, encompassing raw, staging, mart, and feature tables.

• Develop and maintain pipelines and transformations for application, loan, performance, and vendor data.

• Ensure modeling datasets are reproducible and safeguard against training-data leakage.

• Maintain tables that support Credit Risk dashboards and governed metrics.

• Write automated tests for keys, duplicates, ranges, referential integrity, and source reconciliation.

• Keep documentation, lineage, and owner registries up to date.

• Provide vendor oversight by verifying SLAs and reconciling pull counts with vendor invoices.

⛳️ Requirements

• A minimum of 4 years of experience in data engineering or analytics engineering, including a role as the primary owner of a production data platform.

• Proficient in SQL with strong data modeling capabilities, including dimensional models, slowly changing data, and point-in-time snapshots.

• Hands-on experience with a cloud data warehouse, preferably Snowflake.

• Familiarity with a transformation framework such as dbt, incorporating version control, code reviews, and CI.

• Proficient in Python for building pipelines, tests, and automation.

• Proven experience in developing data quality tests and alerts, utilizing dbt tests, Great Expectations, Monte Carlo, Elementary, or custom checks.

• Experience with orchestration tools like Airflow, Dagster, or Prefect.

• An ownership mindset: you identify issues proactively, investigate to the root cause, and implement solutions.

• Strong written communication skills: you can draft a data contract, an incident report, or table documentation that others depend on.

• Bilingual proficiency in Spanish and English is a plus, but not mandatory.

• Experience in fintech or lending, familiarity with credit bureau data, bank data, feature tables, training datasets, model monitoring, lending compliance, BI tools, and Metabase are advantageous, but not required.

🏝️ Benefits

• An opportunity to engage in critical financial products that directly influence customers and drive business growth.

• Complete ownership of the Credit Risk data layer, along with the chance to influence its evolution.

• Challenging opportunities across data pipelines, data quality, monitoring, and modeling datasets.

• A work environment where AI is integral to our processes and development.

• A collaborative, multidisciplinary team that spans Credit Risk, Data Science, Engineering, and Product.

• Fully remote work arrangement.