[Hiring] Director of Data Engineering @Kapitus

Role Description

Kapitus is building a next-generation enterprise data and AI capability 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 looking for a Director of Data Engineering to serve as the senior technical leader for this program: the person who owns how the platform is engineered. You will set the architecture and engineering standards for a Snowflake-centered data platform, lead internal and partner engineering teams across onshore and offshore locations, and stay hands-on enough to review a pull request, challenge a data model, and unblock a pipeline yourself. This is a player-coach role — you will spend real time in the code and the designs, not only in meetings.

You report directly to the program executive leading the Data & AI organization and serve as the technical delivery lead for the transformation program. You are the counterpart to the Technical Program Manager: the TPM owns the integrated plan, the gates, and the commercial controls; you own the architecture, the engineering quality, and the teams that build. Together you make sure what ships is well-built, evidenced, and accepted — not merely finished.

What you will do:

• Architecture and technical leadership
  • Own the engineering and solution architecture of the data platform within approved enterprise and data architecture standards.
  • Translate approved data architecture, MDM, ontology, semantic, and data-contract standards into enforceable engineering patterns.
  • Set and enforce engineering standards: repository structure, branching and CI/CD, code review, testing and data quality checks, naming and documentation, and performance and cost discipline.
  • Lead design reviews and architecture decision forums; document decisions, manage exceptions with owners and expiry dates.
  • Design for reuse: shared ingestion frameworks, certified transformation patterns, common serving structures, and semantic consistency.
  • Stay technically hands-on: review critical code and designs, prototype or intervene on the highest-risk components when needed.
• Engineering delivery
  • Lead the engineering build of governed business data products end to end.
  • Direct the migration and decommissioning of legacy analytics workflows onto the modern platform.
  • Own platform hardening and readiness: environments, security and access patterns, orchestration reliability, and operational readiness.
  • Own engineering reliability for production data services including observability, service-level objectives, incident and problem management.
  • Engineer for the AI/ML workstream: feature-ready data, ML pipeline integration, and the data foundations that GenAI and agentic workloads depend on.
  • Deliver against the program’s milestone gates: vendor verification, business/customer validation, production acceptance.
• Team leadership — onshore and offshore
  • Lead and develop a blended engineering organization: internal engineers, partner delivery pods, and offshore teams across time zones.
  • Make the onshore/offshore model actually work: clean handoffs, clear design specifications before build starts.
  • Uphold separation of duties between build and validation teams.
  • Assess partner engineering quality directly; review their designs and code.
  • Hire, coach, and grow engineers; set clear expectations.
• Cross-functional partnership
  • Partner with the Technical Program Manager on sequencing, capacity, dependency management, and gate readiness.
  • Work with data governance so controls ship with the product.
  • Engage business owners and architecture directly — explain technical trade-offs in business terms.

Qualifications

• 10+ years of data engineering experience
• 5+ years leading data engineering teams through enterprise data platform builds or modernizations.
• Deep hands-on Snowflake expertise with warehouse and database design.
• Hands-on dbt mastery including project architecture and CI/CD integration.
• Broad modern data stack fluency with orchestration and ingestion tooling.
• Strong software engineering fundamentals — expert in SQL and experience with Python.
• Architecture credibility with experience in data modeling at enterprise scale.
• Consulting or professional services delivery background preferred.
• Proven distributed team leadership experience.
• AI/ML platform literacy.
• Strong communication skills.

Requirements

• Experience in financial services in lending, banking, fintech, or another regulated environment.
• SnowPro certifications (Core, Advanced Architect, or Data Engineer) valued.
• Experience with data product operating models.
• FinOps experience measuring and managing cloud data platform cost.
• Experience standing up engineering practices from scratch on a program.

Benefits

• Competitive Base Salary Range of $157,100-$252,000.
• Annual Incentive Compensation Eligibility – Up to 15% annually.
• Comprehensive medical, dental, and employer-paid vision plans through UnitedHealthcare.
• Flexible Spending Account for qualified out-of-pocket expenses.
• Lifestyle Spending Account for reimbursement of well-being expenses.
• 100% Company Paid Insurances including short-term and long-term disability insurance.
• Voluntary Insurance options available.
• Paid Maternity and Parental Leave.
• Commuter Benefits on parking and travel expenses.
• LifeBalance Program offering discounts on various activities.
• Tuition Reimbursement up to $5,000 annually.
• Travel Reimbursement for work-related travel.
• Paid Time Off and Sick Time.
• Retirement Benefits with a 25% match on contributions, up to 6% of annual salary.