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

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 a central Data Engineer, you will sit amongst a team of Data Engineers focused on defining and up-leveling the craft and product of Data Engineering across all of Shopify. Your impact is seen at scale when you ship high quality data assets to Shopify’s Data Warehouse, which get used by thousands of people to perform product analysis, make decisions to support our merchants, and steer strategic decisions of the business. You also achieve with others as you partner with Data Engineers embedded in product teams to share and gather standards and best practices, and perform data architecture duties to uplevel and maintain the high quality of Data Engineering at Shopify.

Example day to day responsibilities include:

  • Partnering as a member of the Analytics Engineering founding team to influence and create this greenfield space at Shopify
  • Working with business partners to understand requirements
  • Working with developers to understand and influence how data is produced
  • Collaborating with data engineers on tooling for automated tasks around consuming, validating raw/modeled data, updating modeled data
  • Designing, building, profiling, and documenting our datasets and the jobs that build them
  • Profiling raw data sets
  • 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
  • Controlling data quality
  • Debating whether leading or trailing commas are better
  • Fixing jobs that are broken in production (wrong data types returned, granularity not as expected, resourcing issues)
  • Collaborate with sister disciplines (Engineering, Data Science) to establish best practices


  • 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
  • Proficient in data architecture and modeling concepts, able to coach and influence others to up-level the craft of Data Engineering
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