Job Description

Who We AreNotion is the collaborative AI workspace where teams and agents think together. We're building one place where your knowledge, projects, meetings, and AI tools live side by side, so work is faster, clearer, and less fragmented. Millions of individuals, small teams, and large companies run their work on Notion.Notinos (our employees) are customer zero in bringing this future of work to life. We care about craft, building things that last, and the belief that great work is still fundamentally human. Our goal isn’t to ship the next feature. Each and every team of Notinos is working to set the standard for how humans work together in the AI era. From building a business’s system of record to making and managing AI agents to automating away the busy work, we care deeply about giving our customers more time for their life’s work.About The RoleAs 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.This role can be based in either San Francisco or New York City. We work from our offices on Mondays, Tuesdays and Thursdays (our Anchor Days) because we do our best thinking and building together in person. We’re looking for someone who’s excited to work alongside the team during those days. What You'll AchievePartner 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 reportingDefine 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 healthDesign privacy-first data architecture, including access controls, PII handling, and governance standardsImplement data quality, testing, and observability systems to ensure sensitive employee data is accurate and trustworthyDesign 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 productionPartner 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 Bring6+ 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-makingHands-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 systemsProficiency in SQL and data modeling, and can build production-grade transformations, define durable metric logic, and write performant queries against large analytical datasetsProficiency in Python (or similar OOP languages) for building data pipelines, automation, and infrastructureExperience designing systems with data quality, testing, monitoring, and SLAs in mindExperience 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 qualityExcellent judgment with sensitive and confidential data, and can implement infrastructure and tooling for

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