Analytics Engineer

Loop is a company specializing in post-purchase commerce, focusing on returns and exchange data. The Analytics Engineer role involves bridging raw data with analyses and data products that enhance merchant outcomes, while collaborating with various teams to maintain and develop data models and infrastructure.

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 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

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), and warehouse optimization
• Business acumen in at least one functional area: Product, GTM, Finance, Marketing, or Customer Experience
• Python experience for data transformation or automation tasks (nice to have, not required)
• 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