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, while unlocking powerful insights to guide the development and improvement of Shopify’s products. You’ll operate in an embedded capacity, or on a central Data Engineering team at Shopify as part of the Data Insights organization (exclusive of Data Platform Engineering, which is part of our Infrastructure organization).

As an embedded Data Engineer, you will work directly on 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. It will also power your own product analysis, dashboard, and report creation from time to time. Our product is designed to empower entrepreneurs. That means the work you do will not only create value for our users but also contribute to global entrepreneurship.

As a central Data Engineer, you will work within a team 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 is used by thousands of people to perform product analysis, make decisions to support our merchants, and steer strategic decisions of the business. You’ll partner with Data Engineers embedded in product teams to share and gather standards and best practices, and perform data architecture duties to up-level and maintain the high quality bar of Data Engineering at Shopify.

To thrive in either an embedded or central role, 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
  • Working with engineers to understand and influence how data is produced, while influencing product and program decisions with data
  • 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
  • Collaborating with other data engineers on tooling for automated tasks related to consuming, validating raw/modeled data, updating modeled data
  • Collaborating with closely tied disciplines (Engineering, Data Science) to establish best practices and support the priorities of the Data Engineering craft
  • Building production-quality dashboards and scalable data products
  • 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)

In a manager role, you’d also be:

  • Providing leadership within your team — both from a technical and people standpoint — to support the mission of the Data Engineering discipline
  • Deploying your team towards data engineering priorities, achieving measurable impact and outcomes.
  • Defining and executing on your team’s deliverables
  • Leading all aspects of performance management, including compensation, performance improvement plans, promotions, and separations

You might be great in this role if you are:

  • Expert in data architecture and modeling concepts, and able to influence others to up-level the craft of Data Engineering
  • Experience creating alignment with senior stakeholders on technical direction for ambiguous problem areas
  • Commercial experience in Data Engineering and/or Analytics Engineering, ​​​​​​and 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
  • High proficiency for product analysis, dashboarding, and reporting

 

For a staff or senior staff role, you’d also have:

  • Proven ability to provide leadership and guidance beyond one-on-one mentorship, and the ability to improve data engineering practices across teams

     

For a manager role, you’d also have:

  • Experience managing high-performing data engineering teams
  • Excitement for managing a remote team; you value collaborating on problems, asking questions, delivering feedback, and supporting others in achieving their goals
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