Data Engineer, People Analytics

Data Engineer, People Analytics ABOUT US: Notion helps you build beautiful tools for your life’s work. In today's world of endless apps and tabs, Notion provides one place for teams to get everything done, seamlessly connecting docs, notes, projects, calendar, and email—with AI built in to find answers and automate work. Millions of users, from individuals to large organizations like Toyota, Figma, and OpenAI, love Notion for its flexibility and choose it because it helps them save time and money. In-person collaboration is essential to Notion's culture. We require all team members to work from our offices on Mondays, Tuesdays, and Thursdays, our designated Anchor Days. Certain teams or positions may require additional in-office workdays. ABOUT THE ROLE: As Notion scales, there is a unique opportunity to build the data foundations for People Analytics that enable better decisions around hiring, performance, compensation, and org design - while maintaining strong privacy guarantees for sensitive employee data. In this role, you will design, build, and operate scalable data systems and pipelines that power People data. Your work will ensure that employee data is reliable, secure, and production-ready, enabling both analytics and AI-driven people workflows across the company WHAT YOU'LL ACHIEVE: - Partner closely with the Chief People Officer and People leadership team to translate strategic questions into clear metrics and data products that inform workforce planning, organization design, performance reviews, compensation discussions, and executive reporting - Define and build ETL/ELT pipelines and infrastructure to ingest and standardize data from HRIS and adjacent systems such as Workday, ATS, payroll, benefits, performance platforms, creating a reliable source of truth for headcount, org structure, hiring, attrition, and talent health - Design privacy-first data architecture, including access controls, PII handling, and governance standards - Implement data quality, testing, and observability systems to ensure sensitive employee data is accurate and trustworthy - Design privacy-aware data models, transformations, semantic layers, and quality checks so sensitive employee data is accurate, timely, well-documented, and governed appropriately with role-based access controls for decision-making and makes AI-powered people tools and automations reliable enough to ship to production - Partner with People Analytics to translate ambiguous requirements into scalable datasets, dashboards, and self-serve data products that give People leaders, People Partners, and managers visibility into workforce trends while reducing manual overhead. SKILLS YOU'LL NEED TO BRING: - 6+ years of experience in analytics engineering, data engineering, people analytics, or a closely related role, with a track record of building trusted datasets and systems for business decision-making - Hands-on experience working with HRIS and adjacent People systems data (for example Workday, ATS (Ashby), payroll, benefits, performance, or engagement platforms) and know how to reconcile definitions across messy, evolving source systems - Proficiency in SQL and data modeling, and can build production-grade transformations, define durable metric logic, and write performant queries against large analytical datasets - Proficiency in Python (or similar OOP languages) for building data pipelines, automation, and infrastructure - Experience designing systems with data quality, testing, monitoring, and SLAs in mind - Experience with modern data tooling and cloud warehouses such as Snowflake, dbt, Airflow, Fivetran, or similar technologies, along with strong instincts around testing, monitoring, and data quality - Excellent judgment with sensitive and confidential data, and can implement infrastructure and tooling for data privacy, access controls, governance, and thoughtful interpretation of people metrics - Effectiveness in partnering directly with the CPO, ... (truncated, view full listing at source)