Credit Risk Data Engineer

🔥 3 hours ago

🌐 Dominican Republic, Argentina, +1 more countries – Remote

🇩🇴 Dominican Republic – Remote 🇦🇷 Argentina – Remote 🇨🇴 Colombia – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🚰 Data Engineer

👻 Ghost score 10%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Kiwi

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2020

🛡️ Insurance

💼 Consulting

💳 Fintech

💰 $75M Debt Financing on 2023-05

Insurance • Consulting • Fintech

Kiwi is a financial technology company that specializes in providing personal loans and credit-building services. Customers can quickly apply for loans online, with amounts available up to $3,000, without affecting their credit scores. The platform offers a fully digital experience, flexible payment options, and automated payment systems to help users manage their loans easily and securely. Kiwi is committed to protecting user data and reporting payments to credit bureaus to assist clients in building their credit history.

📋 Description

• Own the inventory of every credit data source, including TransUnion, Clarity, Plaid, Prism, and internal data from the app and loan servicing • Define required data, grain, freshness, and destination for each source • Write data contracts with engineers building vendor integrations • Confirm raw vendor responses are stored completely for model development and audits • Monitor freshness, volume, fill rate, and schema for every source and model input • Set up alerts for feature fill-rate drops, missing vendor fields, and volume deviations • Track input drift for production models alongside data scientists • Triage data incidents, identify root causes, route fixes, and track closure • Own the Credit Risk layer of the Snowflake warehouse, including raw, staging, mart, and feature tables • Build and maintain pipelines and transformations for application, loan, performance, and vendor data • Keep modeling datasets reproducible and prevent training-data leakage • Maintain tables behind Credit Risk dashboards and governed metrics • Write automated tests for keys, duplicates, ranges, referential integrity, and source reconciliation • Maintain documentation, lineage, and owner registries • Support vendor oversight by checking SLAs and reconciling pull counts against vendor invoices

🎯 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 • Bilingual in Spanish and English is nice to have, not required • Experience in fintech or lending, credit bureau data, bank data, feature tables, training datasets, model monitoring, lending compliance, BI tools, and Metabase are nice to have, not required

🏖️ Benefits

• The opportunity to work on critical financial products with direct impact on customers and business growth • Full ownership of the Credit Risk data layer and the opportunity to shape how it evolves • Meaningful challenges across data pipelines, data quality, monitoring, and modeling datasets • An environment where AI is becoming a core part of how we work and build • A collaborative multidisciplinary team across Credit Risk, Data Science, Engineering, and Product • 100% remote