Senior Engineering Manager-People Analytics

Who we are: 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. Role Summary: 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. What you’ll do: Manage and develop data engineers - 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. - Collaborate with cross-functional teams, such as other data engineers, people analysts, data scientists, and business stakeholders, to translate requirements into production-ready deliverables, and communicate technical trade-offs to non-technical partners. Stay hands on - Write and review production code. - Lead design reviews, code reviews, and technical problem solving. - Step into critical pipelines, models, or AI workflows when needed. Build scalable People data foundations - Design and maintain sustainable data models, pipelines, semantic layers, and testing frameworks. - Establish team practices for documentation, lineage, data quality, and observability. Own engineering standards - Set standards for SQL, Python, dbt, Airflow, Snowflake, testing, documentation, CI/CD, and release management. - Ensure the team ships work that is reliable, maintainable, secure, and understandable.

- Enforce data governance policies and practices to maintain data integrity, security, and compliance with relevant regulations. Support AI enabled analytics - Own technical delivery for AI-assisted workforce analytics and internal tools. - Translate business needs into scalable technical designs, delivery plans, and engineering milestones. - Partner with People Analytics, People leaders, Legal, Compliance, and other stakeholders to deliver trusted workforce insights. - Partner on semantic models, evaluation datasets, testing, and quality controls for AI-assisted analytics. Balance speed and rigor - Create enough process to protect quality, privacy, and trust for sensitive People data without slowing the team unnecessarily. What you’ll need: - 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. - Strong ability to translate business needs into technical architecture. - Experience with sensitive or regulated data and access controls. - Proven ability to coach engineers and build healthy technical culture. - Strong communication with technical and non-technical stakeholders. - Proficiency in relational and cloud database platforms such as Snowflake, Redshift, or GCP. - Thorough knowledge of data modeling, database design, data architecture principles, data operations, and CI/CD.