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Salary: USD 200,000 - 0 / annual
Grüns is a nutrition company focused on making foundational nutrition simple and convenient through comprehensive gummy supplements. As Director of Data Engineering, you will own the company's data platform and lead the team that builds and maintains it.
You will be responsible for the data roadmap and company data model, establishing a single source of truth for core business data. Key responsibilities include refactoring legacy jobs and reducing technical debt to enable platform scalability, maintaining resilient pipelines to ensure data availability and accuracy across the organization, and managing warehouse and infrastructure costs as data volume grows. You will run the intake process and sprint cadence, prioritizing requests by business impact while remaining flexible when priorities shift. You'll lead and coach a small team through code reviews, feedback, and clear growth paths, getting hands-on when needed. Additional responsibilities include connecting new enterprise systems (such as ERP) into the warehouse and making the team AI-native by leveraging AI agents for development, incident investigation, and data quality.
The role is fully remote as part of Grüns' remote HQ, with a high-trust work environment and biannual in-person off-sites for team connection.
REQUIREMENTS:
- 8+ years in data architecture or data engineering, including 3+ years leading teams
- Experience building and scaling cloud data platforms (BigQuery or similar)
- Strong proficiency in SQL, Python, and dbt or similar modeling framework
- Deep data modeling experience, starting from business concepts before tables
- Demonstrated experience building data quality, governance, and monitoring practices that teams trust
- Experience running sprint-based intake and prioritization for teams serving multiple stakeholders
- Comfort with ELT tools such as Fivetran and Orbitrail, and enterprise systems (ERP experience is a plus)
- Background in DTC or CPG preferred
Desired approach: starts with business questions before selecting technology, prioritizes fundamentals (integrity, governance, observability), treats data trust issues as urgent, prioritizes ruthlessly, stays hands-on while leading, coaches engineers toward senior and management roles, and uses AI agents daily.
