Senior Engineering Manager (People Analytics)

• 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 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

Benefits

• Generous vacation and holidays
• Paid parental leave for eligible employees
• 401(k) and education on retirement planning
• Monthly contribution up to $200 to help you pay off your student loans
• Great health & well-being benefits including: telehealth parental support, subsidized gym program
• Comprehensive health, vision, dental, life insurance, and disability benefits
• Fertility and family planning options
• Flexible time off
• Free financial classes
• Tuition reimbursement on approved programs, up to $5,250 per year- Proven ability to coach engineers and build healthy technical culture
• Experience with data quality, testing, lineage, observability, and production support
• Strong analytical and problem-solving abilities, with the capability to simplify complex issues into actionable plans
• Strong communication with technical and non technical stakeholders
• 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
• Strong ability to translate business needs into technical architecture
• Proficiency in data engineering tech stack: Python / SQL / dbt / Airflow / Gitlab
• 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
• Experience with sensitive or regulated data and access controls
• 5+ years managing or formally leading engineers
• Experience designing dimensional models, semantic layers, data marts, or analytical data products
• People analytics, HR data, compensation, talent, workforce planning, or Workday experience
• Experience in fintech, banking, or regulated environments
• Experience with Snowflake Cortex AI, Streamlit, semantic models, or evaluation frameworks
• Experience building AI, LLM, RAG, or natural language analytics products