Credit Risk Data Engineer

Kiwi

. Own the inventory of every credit data source, including TransUnion, Clarity, Plaid, Prism, and internal data from the app and loan servicing .

Posted 9/30/2026full-timeRemote • Dominican Republic, Argentina, ColombiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in data engineering with a focus on SQL, data modeling, and cloud data warehousing, particularly in Snowflake. Capable of building and maintaining data pipelines, ensuring data quality, and effectively communicating technical documentation.

Highest-signal resume keywords

Expert SQLData ModelingSnowflakePythonData Quality Testing

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringDimensional ModelsSlowly Changing DataPoint-In-Time SnapshotsData ContractsAutomated TestingFeature TablesModel MonitoringIncident ManagementData Pipeline Development

Soft Skills

Ownership MindsetClear Written Communication

Tools & Technologies

DbtAirflowDagsterPrefectGreat ExpectationsMonte CarloElementaryCI/CD

Industry Keywords

FintechLendingCredit Bureau DataBank DataBI ToolsMetabaseLending Compliance

Tech Stack

Tools & technologies

AirflowCloudPythonSQL

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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