AML Compliance Manager
Western UnionLead Western Union’s law enforcement partnerships, intelligence sharing, and subpoena operations while strengthening financial crime controls. This hybrid role is based in Atlanta.
Open
Kiwi is a fintech company focused on personal lending and credit building. Its digital platform lets customers apply online for loans of up to $3,000 without affecting their credit scores, while offering flexible repayment options and automated payments. Kiwi also reports eligible payments to credit bureaus, helping customers work toward a stronger credit history, with data protection integrated into the lending experience.
Credit Risk Data Engineer at Kiwi – Remote in Dominican Republic
Own Kiwi’s credit-risk data foundation for lending decisions, from Snowflake pipelines and data quality monitoring to reproducible modeling datasets. This remote role supports teams across the Dominican Republic, Argentina, and Colombia.
Description
- Manage the complete inventory of credit data sources, including TransUnion, Clarity, Plaid, Prism, and internal application and loan-servicing systems
- Specify the required data, grain, freshness, and destination for each source
- Create data contracts with engineers responsible for vendor integrations
- Verify that raw vendor responses are fully retained for model development and audit needs
- Monitor source and model-input freshness, volume, fill rate, and schema
- Configure alerts for declining feature fill rates, missing vendor fields, and unusual volume changes
- Monitor production-model input drift in partnership with data scientists
- Investigate data incidents, determine root causes, coordinate remediation, and track resolution
- Own the Credit Risk layer of the Snowflake warehouse across raw, staging, mart, and feature tables
- Develop and maintain pipelines and transformations for application, loan, performance, and vendor data
- Ensure modeling datasets remain reproducible and protected from training-data leakage
- Maintain the tables that power Credit Risk dashboards and governed metrics
- Develop automated checks for keys, duplicates, value ranges, referential integrity, and source reconciliation
- Keep documentation, data lineage, and ownership registries current
- Support vendor governance by reviewing SLAs and reconciling pull volumes with vendor invoices
Requirements
- At least four years of data engineering or analytics engineering experience, including primary ownership of a production data platform
- Expert-level SQL and strong data modeling capabilities, including dimensional models, slowly changing data, and point-in-time snapshots
- Practical experience with a cloud data warehouse, preferably Snowflake
- Experience with a transformation framework such as dbt, along with version control, code review, and CI practices
- Python experience for building pipelines, tests, and automation
- Demonstrated success implementing data-quality tests and alerting with dbt tests, Great Expectations, Monte Carlo, Elementary, or custom checks
- Experience using an orchestration platform such as Airflow, Dagster, or Prefect
- A strong ownership approach, including proactively detecting issues, tracing root causes, and driving them to resolution
- Clear written communication skills for producing dependable data contracts, incident notes, and table documentation
- Spanish and English fluency is helpful but not required
- Experience with fintech or lending, credit bureau and bank data, feature tables, training datasets, model monitoring, lending compliance, BI tools, or Metabase is helpful but not required
Benefits
- Work on important financial products with a direct effect on customers and business growth
- Take full ownership of Kiwi’s Credit Risk data layer and influence its development
- Tackle substantive problems across pipelines, data quality, monitoring, and modeling datasets
- Join a workplace where AI is becoming central to how the team works and builds
- Collaborate with colleagues across Credit Risk, Data Science, Engineering, and Product
- Work fully remotely
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