Staff Analytics Engineer

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.

Data is the voice of all our customers – merchants, buyers, developers – and hearing this voice clearly is critical to us making commerce better for them. As an Staff Analytics Engineer, your primary responsibilities will be to build a first class data warehouse. Our Analytics Engineering team will unlock a large number of analyses on varied topics not only now but in the future and will also provide a complete view of Shopify’s product and business.

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 set 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)
Creating and implementing architecture and standards for all the Analytic Engineer team.
Collaborate with sister disciplines (Engineering, Data Science) to establish best practices.

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

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