Moving at our pace brings a lot of change, complexity, and ambiguity—and a little bit of chaos. Shopifolk thrive on that and are comfortable being uncomfortable. That means Shopify is not the right place for everyone.

Before you apply, consider if you can:

  • Care deeply about what you do and about making commerce better for everyone
  • Excel by seeking professional and personal hypergrowth
  • Keep up with an unrelenting pace (the week, not the quarter)
  • Be resilient and resourceful in face of ambiguity and thrive on (rather than endure) change
  • Bring critical thought and opinion
  • Embrace differences and disagreement to get shit done and move forward
  • Work digital-first for your daily work

About the role

About the role

Data is a crucial part of Shopify’s mission to make commerce better for everyone. We organize and interpret petabytes of data to provide solutions for our merchants and stakeholders across the organization.

As a Data Engineer at Shopify, your primary responsibility will be to contribute to Shopify’s Data Warehouse. Your work will unlock powerful insights to guide the development and improvement of Shopify’s products.

As an embedded Data Engineer, you will work directly building foundations for products that transform merchants’ lives. You’ll collaborate with a multidisciplinary team of professionals that can include Product Data Scientists, Machine Learning Engineers, Business Analysts, and Product Management. The data you shape will be used to power product analysis, dashboards, and reports. You will also contribute by performing product analysis and creating dashboards and reports yourself from time to time. Get excited about flexing these analysis and reporting muscles, while also evangelizing the values and excellence in the Data Engineering craft to your multidisciplinary peers in a fast moving environment with competing priorities.

Our product is designed to empower entrepreneurs. Consequently, the work you do will not only create value for our users but also contribute to global entrepreneurship. To thrive here, you need to be dedicated to your craft and committed to constant development. You should be an independent thinker who can solve complex problems and handle a bit of chaos without breaking a sweat.

Example day to day responsibilities include:

  • Working with business partners to understand business and product objectives and identify the data needed to support them
  • Designing, building, implementing, and documenting data models
  • Writing data transformations using dbt or Spark
  • Shipping data pipelines including real-time streaming and batch processing
  • Optimizing data transformation pipelines to increase freshness or reduce computational time/cost
  • Working with engineers to understand and influence how data is produced
  • Collaborating with other data engineers on tooling for automated tasks around consuming, validating raw/modeled data, updating modeled data
  • Subscribing to and implementing architecture and standards following the Data Engineer craft at Shopify
  • Collaborating with sister disciplines (Engineering, Data Science) to establish best practices and evangelize the values and priorities of the Data Engineering craft
  • Partnering closely with product, engineering and other business leaders to influence product and program decisions with data
  • Building production-quality dashboards and scalable data products


  • Commercial experience in Data Engineering, and/or Analytics Engineering, ​​​​​​building scalable data warehouses
  • Dimensional Modeling (Star Schema, Kimball, Inmon)
  • Advanced SQL skills (ease with window functions, defining UDFs)
  • Exposure to Data Engineering tooling: ingesting, testing transformations, lineage, orchestration, publishing data, metric layers
  • Hands-on experience implementing real-time and batch data pipelines with tight SLOs and complex transformation requirements
  • Fantastic collaboration and communication skills, demonstrated by successful large-scale projects spanning multiple teams
  • Technical thought leader, comfortable navigating ambiguity and mentoring various level of team members
  • Aptitude for product analysis, dashboarding, and reporting
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