Senior Engineering Manager-People Analytics

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Who we are:

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.


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

Preferred Experience

• 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