Staff Data Engineer - People Analytics

Most data roles at scale ask you to go deep on one slice of the stack. This one asks you to be deep in ownership and broad in craft. You'll move across the full data lifecycle, building pipelines, shipping internal tools, running analyses, designing forecasts, and presenting findings directly to senior leaders, sometimes all in the same week. The work is internal-facing and focused on the people who make Shopify run: how they're hired, how they grow, how they're paid, and how the company plans where to invest its talent. This is not HR reporting: the domain is people, but the work is production data systems, modeling, tooling, and decision support. If you're a builder who's curious about people and tired of staying in one swim lane, this role is built for you. What you'll be working on You'll join a small, cross-functional team that builds the data systems and internal products Shopify's leadership relies on to understand its workforce. The team is intentionally generalist. Data engineers and data scientists work alongside each other and regularly cross into each other's territory. Your work will touch performance review cycles, company-wide surveys, compensation system, recruiting and interviewing data, executive talent reporting, operational alerting, and headcount planning systems. At this level, you'll own product areas end-to-end. That means defining the problem, shipping the solution, presenting it to discipline leaders and senior executives (including the CEO), and pivoting as the company's needs shift. You'll be trusted to operate independently and represent your work to the people whose decisions depend on it. Because the team runs much of its own data infrastructure rather than relying on central platforms, you'll work across the full data stack, from extractors and warehouses to notebooks, dashboards, forecasts, and internal tooling. It's a startup-within-Shopify environment: lighter process, high accountability, faster pace, and broad surface area for someone who wants it. You'll need to have Strong fundamentals as a data generalist who has shipped across the full data lifecycle: pipelines, analysis, dashboards, forecasting, data tools, and some predictive modeling Deep proficiency in SQL, Python, dbt, and Airflow A builder's mindset. You'd rather turn analyses into a maintained product, not stopping at a deck or one-off readout. Genuine ownership and agency: comfort owning a roadmap, making calls without a long approval chain, and interfacing directly with senior executives Strong written and verbal communication, including the ability to explain complex analytical work to non-technical leaders Strong judgement with sensitive data, including data quality, access control, privacy, validation and communicating uncertainty clearly. It's great if you also have Experience with Apache Flink A natural curiosity about people, organizations, and what drives them Experience in a startup or other small, generalist environment where you wore many hats A background that does not fit neatly into one box: data engineering, analytics engineering, product data science, or another quantitative builder path can all work here.