Senior Data Engineer, Multiple Teams (Remote, Americas)

Company Description

About Shopify

Opportunity is not evenly distributed. Shopify puts independence within reach for anyone with a dream to start a business. Since 2006, we’ve grown to over 10,000 employees and generated over $500 billion in sales for millions of merchants in 175 countries. Every 28 seconds, an entrepreneur on Shopify makes their first sale.

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 — and embrace differences and disagreement to get shit done and move forward
• Work digital-first for your daily work

Job Description

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 Senior 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 as either an either 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, as well as your own product analysis, dashboard and report creation from time to time. Our product is designed to empower entrepreneurs and thus your work creates value for users, and contributes to global entrepreneurship. 

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 is 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 up-level and maintain the high quality of Data Engineering at Shopify. 

To thrive in either 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:

• Designing, building, implementing, and documenting data models

• Working with business partners to understand business and product objectives, identify the data needed to support them, while influencing product and program decisions 

• 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 around consuming, validating raw/modeled data, updating modeled data

• Collaborating with sister disciplines (Engineering, Data Science) to establish best practices and evangelize the values and 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)

Qualifications

• Commer