United States onlyLoop 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
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 the team develop shared norms around where and how they add real value.
Your Experience:
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), and warehouse optimization.
Python experience for data transformation or automation tasks (nice to have, not required).
Business acumen in at least one functional area: Product, GTM, Finance, Marketing, or Customer Experience.
How You Work:
Self-managing. You don’t need someone to tell you what to do next, and you know when to ask for input versus when to just make a call.
Clear communicator. You can explain a data modeling decision to an engineer and to a non-technical analyst without changing what you actually mean.
Pragmatic. You understand the difference between a solution that’s technically interesting and one that solves the problem and holds up over time.
Curious about AI. You’re already using AI tools in your work and thinking about how to get more out of them, not waiting to be told to.
Progress-oriented. You understand that rigor and risk scale together. Some solutions can be good enough to just make progress today, while others need to be reliable day in and day out.
Nice to have:
Experience with dimensional modeling and data warehousing concepts.
Experience building self-serve content in Looker, Hex, or GoodData for non-technical end users.
SaaS, e-commerce, or startup background.
Experience with project management and cross-functional execution.
Canada
Australia only
United Kingdom only