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Kapitus is building a next-generation enterprise data and AI capability for our financial services organization. This is a multi-year modernization program that replaces hundreds of legacy analytics workflows with governed, reusable business data products on a modern cloud data platform, stands up an AI/ML platform, and moves the organization from producing reports to producing decisions.
We are hiring Data Engineers to build that platform and the products on it. The core stack is Snowflake and dbt, organized around a layered medallion architecture bronze/silver/gold-style layers extended with governed conformed entities, certified semantic definitions, and reusable serving structures. You will build ingestion and transformation pipelines, model data across layers, migrate legacy workflows onto the platform, and ship data products through evidence-based acceptance gates.
You report to the Director of Data Engineering, who owns the platform architecture and engineering standards. This is a delivery role on a program that runs to tight timelines and real acceptance criteria: your work is done when it is validated, evidenced, and accepted code complete is not product complete. You will work alongside internal engineers and consulting-partner teams across onshore and offshore locations, all held to the same engineering bar.
Senior Data Engineers additionally own complex workstreams or product slices, lead technical design within the approved architecture, review other engineers’ work, mentor team members, and drive difficult migrations or production issues to closure.
What you will do:
Build
Develop ELT pipelines on Snowflake and dbt: source ingestion, layered transformations through the medallion zones, and governed serving structures for BI, API, ML, and AI consumption.
Model data within the platform’s layered architecture — staging, conformed core entities, dimensional and product-level models — following approved modeling standards for grain, keys, and naming.
Implement approved data contracts, conformed entity definitions, keys, relationships, and certified semantic definitions consistently across models; do not create local definitions when a governed one exists.
Migrate legacy analytics workflows (e.g., Alteryx) onto the modern stack: analyze the existing logic, implement it correctly in dbt and Snowflake, prove parity where required, and support retirement of the legacy asset.
Implement data quality as code: dbt tests, source freshness checks, reconciliation and validation queries, and pipeline observability — quality controls ship with the pipeline, not after it.
Build for reuse: contribute to shared ingestion frameworks, certified transformation patterns, and common serving structures rather than one-off solutions.
Engineer
Work software-engineering style: Git-based workflows, peer code review, CI/CD, environment promotion, and documentation that lets someone else operate what you built.
Follow and help improve the platform’s engineering standards — repository structure, dbt project conventions,…