About the role
Loop sits in a rare position in post-purchase commerce: we have return and exchange data at a scale and depth that no one else can replicate. That asset only matters if we can make it useful, and that’s where this role lives. As an Analytics Engineer at Loop, you’ll be the connective tissue between raw data and the analyses, dashboards, and data products that drive real merchant outcomes. Our data stack powers everything from self-serve analytics for our internal teams to the machine learning models that underpin Loop Intelligence, our merchant-facing insights layer. You’ll be building and maintaining the foundation that makes all of that possible. In this role, you’ll work with meaningful autonomy, own new areas of the dbt project, and collaborate closely with your fellow analytics engineers, data analysts, data engineers, and the broader Data Team. You’ll also be expected to bring your own judgment: when to keep it simple, when something needs to be built the right way, and when to raise a flag. We’ve laid out the experience we think is important to set you up for success in this role. But we appreciate that different humans will solve problems in different ways, so we don’t expect you to fit exactly in a box of
requirements. At Loop, we’re intentional about the way we work so that we can do our best work. We call this our Blended Working Environment. We work from our HQ in Columbus, OH, or one of our Hub or Secluded locations, and are distributed throughout the United States, select Canadian provinces, and the United Kingdom. For this position, we are looking for a teammate located in the United States. Our Tech Stack dbt, Snowflake, Hex, Fivetran, Streamkap, Secoda, GoodData, Gitlab \n What You’ll Do: Design, develop, and maintain production-quality dbt models with a focus on performance, readability, and long-term maintainability. Translate business
requirements into clean, well-documented data models that analysts and downstream consumers can rely on, including ownership of code reviews and our semantic and metrics layers. Collaborate with Data Engineering on ingestion pipelines, Fivetran and Streamkap connectors, and architecture decisions upstream of modeling, with a practical understanding of Snowflake performance and warehouse costs. Support a dbt contributor base that extends beyond the analytics engineering team, keeping Data Team contributors unblocked and helping them grow their skills. Contribute to the data foundation behind Loop Intelligence, including merchant outcome measurement and shopper-level cohort analyses. Apply data operations fundamentals across your work: Gitlab, CI/CD, automated testing, and documentation that make our data assets easier to discover and use. Use AI tools actively in your workflow and help