Описание

Staff Data Engineer role for Shopify's People Analytics team, focused on building and owning production data systems, pipelines, internal tools, and decision-support products. The position requires strong SQL, Python, dbt, and Airflow skills, with work spanning pipelines, dashboards, forecasting, and some predictive modeling.

Responsibilities

Build and own production data systems, pipelines, internal tools, and decision-support products
Move across the full data lifecycle, building pipelines, shipping internal tools, running analyses, designing forecasts, and presenting findings directly to senior leaders
Work with performance review cycles, company-wide surveys, compensation systems, recruiting and interviewing data, executive talent reporting, operational alerting, and headcount planning systems
Own product areas end-to-end, defining the problem, shipping the solution, presenting it to discipline leaders and senior executives (including the CEO), and pivoting as company needs shift
Operate independently and represent work to decision-makers
Work across the full data stack, from extractors and warehouses to notebooks, dashboards, forecasts, and internal tooling

Requirements

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: would 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

Nice to 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