Senior Analytics Engineer, Instacart

• 5+ years of experience in Analytics Engineering or a closely related role, with ownership of production-grade analytical data models
• Expert-level SQL skills and deep experience designing dimensional models (star schemas, fact/dimension tables, SCDs)
• Strong proficiency with dbt, including project structure, testing, documentation, and macros
• Experience with Snowflake or similar cloud data warehouses
• Solid understanding of marketing data and metrics, including paid media performance, attribution concepts, and channel-level measurement
• Demonstrated ability to set standards, guide execution, and mentor others on analytics engineering best practices
• Excellent judgment and product thinking—you know how to balance speed, correctness, and long-term maintainability

Desirable

• The Marketing Enablement & Technology (MET) team sits natively within Instacart's Marketing organization owning the data foundations that power Paid Marketing, SEO, and Retailer Marketing attribution
• These datasets directly inform how we allocate hundreds of millions of dollars in marketing spend and how we measure growth
• We're hiring a Senior Analytics Engineer to own and evolve the marketing analytics data foundation
• In this role, you'll design high-quality, business-aware data models that Data Scientists and Analysts trust, while also providing technical leadership across marketing data modeling—setting standards, guiding execution, and raising the bar for how data is built, tested, documented, and delivered
• Analytics Data Modeling: Design, build, and maintain high-quality dimensional and semantic data models using dbt that support marketing analytics across Paid Marketing, SEO, Retailer Marketing, and attribution
• Cross-functional Partnership: Work closely with Data Scientists, Analysts, and Marketing stakeholders to understand analytical needs, translate business questions into data requirements, and deliver trusted, decision-ready data assets
• Metrics & Definitions Ownership: Lead efforts in metric definition, business logic standardization, testing, and documentation to ensure consistent, trusted reporting across marketing
• Technical Leadership: Set standards for analytics engineering best practices—including code review, testing frameworks, and modeling patterns—to ensure scalability, reliability, and maintainability across the marketing data ecosystem
• Model Evolution & Optimization: Continuously improve existing data models to reduce complexity, improve query performance, and increase analyst self-service capabilities
• Scalable Analytics Infrastructure: Develop scalable analytics engineering patterns, including dimensional models, semantic layers, testing frameworks, and monitoring approaches, to support evolving marketing measurement needs
• AI & ML Enablement: Partner with Data Scientists & Analysts to build and maintain curated datasets that provide context for ML & AI-driven marketing applications