> Markdown version of [/jobs/ext/4620230-data-engineer](https://www.wearedevelopers.com/jobs/ext/4620230-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** KAPITUS LLC - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $96,300.0 - $154,400.0 - **Contract:** Permanent contract - **Skills:** Query Performance, Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Automation of Tests, Code Review, Continuous Integration, Information Engineering, Database Schema, Python (Programming Language), Machine Learning, Metadata Repositories, Operational Databases, Performance Tuning, Standard Sql, Azure Machine Learning, SAS (Software), SQL Stored Procedures, SQL Databases, SQL Server Integration Services, Tableau (Software), Snowflake, Git, Git Flow, Data Pipelines, Api Management, Alteryx - **Published:** November 7, 2026 - **Apply:** https://startup.jobs/data-engineer-snowflake-dbt-kapitus-10709923 ## About the Role * 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, tasks/streams or equivalent patterns, and loading/unloading at production scale. * Production dbt experience building and maintaining real dbt projects: layered model organization, incremental models, tests and documentation, macros, and working in a shared multi-developer project with CI. * Expert SQL skills complex transformations, window functions, performance tuning, and the judgment to know when SQL is the wrong tool. * Solid Python experience for data engineering ingestion utilities, API integrations, automation, and orchestration code. * Layered/medallion architecture experience. You have built in a bronze/silver/gold or equivalent layered warehouse and understand why layer discipline, grain, and conformed entities matter; experience with governed variants incorporating conformed/core entity models, certified metrics, semantic definitions, or data contracts is a strong plus. * Engineering fundamentals Git, code review, CI/CD, environment promotion, and automated testing as normal practice, not aspiration. * Data quality and verification mindset you are someone who thoroughly tests and reconciles your own work and produces complete verification evidence that a pipeline is correct; formal acceptance validation is performed independently. * Clear written communication for design notes, pull request descriptions, and documentation that hold up across time zones. Additional Qualifications: * Experience migrating legacy analytics workloads (Alteryx, SSIS, SAS, stored-procedure estates, or similar) to a modern cloud stack, including parity testing against the legacy output. * Experience in financial services, lending, banking, fintech, or another regulated environment including working with sensitive data classifications and audit expectations. * Consulting or client-facing delivery experience. You have built under tight timelines and contractual acceptance criteria, and are comfortable with milestone-driven delivery. * Orchestration experience (Airflow, Dagster, or similar), CDC/ingestion tooling, and streaming or near-real-time patterns. * Exposure to data catalogs and lineage tooling, semantic/BI layers (e.g., Tableau, Sigma, or similar), and ML/AI data needs - feature pipelines, training datasets, retrieval foundations. * SnowPro or dbt certifications - valued as evidence of rigor, not as a substitute for demonstrated delivery experience. ## Description 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.