Analytics Engineer

5d5 days ago

Loop

Remote · Full-time · $125,000 – $175,000

About this role

Loop sits in a rare position in post-purchase commerce with return and exchange data at a scale and depth that no one else can replicate. As an Analytics Engineer, 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 self-serve analytics for internal teams and machine learning models for Loop Intelligence.

You’ll build and maintain the foundation that makes all of this possible, working with meaningful autonomy. Own new areas of the dbt project while bringing your own judgment on when to keep it simple or build the right way. Collaborate closely with fellow analytics engineers, data analysts, data engineers, and the broader Data Team.

Loop’s Blended Working Environment allows work from HQ in Columbus, OH, or Hub or Secluded locations, distributed throughout the United States. For this position, we seek a teammate located in the United States. Our tech stack includes dbt, Snowflake, Hex, Fivetran, Streamkap, Secoda, GoodData, and Gitlab.

We’ve laid out experience important for success, but appreciate different humans solve problems differently, so don’t expect an exact fit. Support a dbt contributor base beyond the analytics engineering team, keeping them unblocked. Use AI tools actively and help develop team norms for their value.

Requirements

3+ years of hands-on experience as an analytics engineer, data engineer, or equivalent.
2+ years of experience working with dbt in a production environment, including models, sources, tests, and documentation.
Strong SQL skills and meaningful experience with data warehouse design (Snowflake experience is a plus).
Familiarity with data operations fundamentals: Git workflows, CI/CD, and automated testing applied to data.
Exposure to data engineering concepts, including ETL/ELT pipelines, connector tooling (Fivetran, Streamkap, or similar).

Responsibilities

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 semantic and metrics layers.
Collaborate with Data Engineering on ingestion pipelines, Fivetran and Streamkap connectors, and architecture decisions upstream of modeling, with 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 the team develop shared norms around where and how they add real value.