Data Engineer - Snowflake & DBT

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Candidates with additional questions or concerns regarding any recruiting communications or Kapitus’ recruiting process in general should email [email protected]   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, testing requirements, and performance and cost discipline (warehouse sizing, query efficiency, clustering choices). Tune what you ship: profile query performance, manage Snowflake compute cost consciously, and treat efficiency as part of correctness. Support what you ship in production: use observability to diagnose failures, participate in incident and root-cause analysis, implement durable corrective actions, and prevent recurring defects rather than repeatedly patching symptoms. Surface risks and ambiguity early — resolving a problem before build is cheaper than discovering it during validation or production. Deliver Deliver against the program’s acceptance gates: build to approved designs and acceptance criteria, produce developer verification evidence for your work, and support independent acceptance validation — engineers test their own work thoroughly but do not independently approve it for acceptance. Work from design specifications and data contracts; when a specification is ambiguous or wrong, raise it and get it resolved rather than improvising silently. Collaborate across a distributed team — internal and partner engineers, onshore and offshore — with clean handoffs, clear written communication, and deliberate use of overlap hours. Estimate your work honestly, flag variance early, and close defects from validation findings promptly and with root cause understood. What we’re looking for: 5+ years of hands-on data engineering experience building production data pipelines and warehouse models. Senior-level candidates (8+ years) are considered for Senior Data Engineer and should demonstrate workstream-level technical leadership, design ownership, code-review responsibility, and mentoring — not simply additional years of experience. Strong hands-on Snowflake experience including warehouse and database objects, performance and cost-aware SQL, role-based access concepts, task