Analytics Engineer

Who you are

• 3–5 years of experience in Analytics Engineering, Data Engineering, or closely related roles, with hands-on ownership of production data models and pipelines
• Strong SQL skills and hands-on experience building well-architected dimensional data models (star schemas, fact/dimension tables, SCDs)
• Hands-on experience with the modern data stack, including dbt, Snowflake, and Airflow
• Working knowledge of marketing data and metrics — including paid media performance, attribution concepts, and channel-level measurement — or the ability to ramp quickly
• Demonstrated ability to deliver complete data models and pipelines independently, manage multiple priorities, and write clean, well-documented, well-tested code
• Effective communicator who can collaborate across data, marketing, and engineering teams to understand requirements and deliver solutions
• Experience partnering with Data Scientists on experimentation, attribution, or incrementality measurement
• Familiarity with marketing platforms (ie Google Ads, Meta, Google Analytics, SEO tooling)
• Python experience for automation or advanced transformations
• Comfort using AI-assisted developer tools to improve code quality and speed (e.g., generating tests, validating logic, comparing suggestions)
• Experience contributing to analytics engineering best practices such as testing, documentation, or data quality standards

What the job involves

• We're hiring an Analytics Engineer II to help build and evolve the marketing analytics data foundation.
• In this role, you'll independently design and deliver high-quality, business-aware data models and pipelines that Data Scientists and Analysts trust — owning your work end-to-end, raising the quality bar through code reviews and design discussion, and growing toward broader technical ownership of marketing data
• Marketing Analytics Data Development: Independently build and maintain high-quality dimensional data models and ETL pipelines that support marketing analytics across Paid Marketing, SEO, Retailer Marketing, and attribution — delivering complete data assets end-to-end with minimal oversight
• Cross-functional Partnership: Work closely with Data Scientists, Analysts, and Marketing stakeholders to translate analytical needs and business questions into data requirements, and deliver trusted, decision-ready datasets
• Data Quality Ownership: Own data quality for the models you build — writing tests, documentation, and monitoring, and resolving data issues at their root cause across the marketing workflows you support
• Code Review & Design: Conduct thorough code reviews, write and share designs publicly before building, and apply simple, reusable modeling patterns that leave the codebase better than you found it
• Pipeline Evolution & Observability: Improve existing marketing data pipelines to reduce manual effort and improve performance, enhance observability (logging, metrics, freshness and quality checks), and participate in incident response for the data platform