About the role
from listingOpportunity is not evenly distributed. Shopify puts independence within reach for anyone with a dream to start a business. We propel entrepreneurs and enterprises to scale the heights of their potential. Since 2006, we’ve grown to 10,000 employees and generated over $496 billion in sales for millions of merchants in 175 countries. This is life-defining work that directly impacts people’s lives as much as it transforms your own. This is putting the power of the few in the hands of the many, is a future with more voices rather than fewer, and is creating more choices instead of an elite option.
About You
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 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
Qualifications
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
Originally posted on Himalayas