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, 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.

• Write and review production code.
• Lead design reviews, code reviews, and technical problem solving.
• Step into critical pipelines, models, or AI workflows when needed.

• Design and maintain sustainable data models, pipelines, semantic layers, and testing frameworks.
• Establish team practices for documentation, lineage, data quality, and observability.

• 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.

• 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

• Create enough process to protect quality, privacy, and trust for sensitive People data without slowing the team unnecessarily.

• 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.
• Strong analytical and problem-solving abilities, with the capability to simplify complex issues into actionable plans.

• People analytics, HR data, compensation, talent, workforce planning, or Workday experience
• Experience building AI, LLM, RAG, or natural language analytics products
• Experience with Snowflake Cortex AI, Streamlit, semantic models, or evaluation frameworks
• Experience in fintech, banking, or regulated environments