Backed by IVP.
About the role
This role will lead the data engineering function supporting People Analytics, including AI-assisted workforce analytics on Snowflake. This is a player-coach role requiring hands-on technical leadership plus people leadership, with strong business partnership and the ability to balance speed, quality, governance, and innovation. The base pay range for this role is listed below. Final base pay offer will be determined based on individual factors such as the candidate’s experience, skills, and location.
What they're looking for
- A bachelor's degree in Computer Science, Data Science, Engineering, or a related field
- 7+ years in data engineering, analytics engineering, or data platform engineering
- 5+ years managing or formally leading engineers
- Proficiency in data engineering tech stack: Python / SQL / dbt / Airflow / Gitlab
- Experience designing dimensional models, semantic layers, data marts, or analytical data products
- Experience with data quality, testing, lineage, observability, and production support
More about this role
Shape a brighter financial future with us.
Together with our members, we’re changing the way people think about and interact with personal finance.
We’re a next-generation financial services company and national bank using innovative, mobile-first technology to help our millions of members reach their goals. The industry is going through an unprecedented transformation, and we’re at the forefront. We’re proud to come to work every day knowing that what we do has a direct impact on people’s lives, with our core values guiding us every step of the way. Join us to invest in yourself, your career, and the financial world.
This role will lead the data engineering function supporting People Analytics, including AI-assisted workforce analytics on Snowflake. This is a player-coach role requiring hands-on technical leadership plus people leadership, with strong business partnership and the ability to balance speed, quality, governance, and innovation.
- Manage, coach, and grow a team of data engineers.
- Set expectations for quality, collaboration, delivery, and technical ownership.
- Create a strong engineering culture where people solve hard problems, move quickly, and enjoy the work.
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