Analytics Engineer
TLDR
The role focuses on creating analytics solutions and dashboards while providing autonomy and collaboration across teams to drive impactful insights.
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Design, develop, and maintain production-quality dbt models with a focus on performance, readability, and long-term maintainability.
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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.
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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.
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Support a dbt contributor base that extends beyond the analytics engineering team, keeping Data Team contributors unblocked and helping them grow their skills.
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Contribute to the data foundation behind Loop Intelligence, including merchant outcome measurement and shopper-level cohort analyses.
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Apply data operations fundamentals across your work: Gitlab, CI/CD, automated testing, and documentation that make our data assets easier to discover and use.
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Use AI tools actively in your workflow and help the team develop shared norms around where and how they add real value.
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3+ years of hands-on experience as an analytics engineer, data engineer, or equivalent.
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2+ years of experience working with dbt in a production environment, including models, sources, tests, and documentation.
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Strong SQL skills and meaningful experience with data warehouse design (Snowflake experience is a plus).
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Familiarity with data operations fundamentals: Git workflows, CI/CD, and automated testing applied to data.
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Exposure to data engineering concepts, including ETL/ELT pipelines, connector tooling (Fivetran, Streamkap, or similar), and warehouse optimization.
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Python experience for data transformation or automation tasks (nice to have, not required).
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Business acumen in at least one functional area: Product, GTM, Finance, Marketing, or Customer Experience.
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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.
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Clear communicator. You can explain a data modeling decision to an engineer and to a non-technical analyst without changing what you actually mean.
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Pragmatic. You understand the difference between a solution that’s technically interesting and one that solves the problem and holds up over time.
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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.
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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.
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Experience with dimensional modeling and data warehousing concepts.
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Experience building self-serve content in Looker, Hex, or GoodData for non-technical end users.
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SaaS, e-commerce, or startup background.
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Experience with project management and cross-functional execution.
Loop builds a connected commerce operations suite that helps merchants streamline their shopping experiences by managing returns, order tracking, and fraud prevention. Serving over 5,000 trusted brands on Shopify, Loop empowers merchants to make smarter decisions and enhance customer satisfaction.
- Founded
- Founded 2017
- Employees
- 11-50 employees
- Industry
- Internet Software & Services
- Total raised
- $11M raised