Analytics Engineer, Instacart

• If you thrive in a fast-paced environment, enjoy building reliable datasets from messy real-world signals, and want to drive visible impact across a high-ownership domain, we’d love to hear from you
• 4+ years of experience in analytics engineering, data engineering, or BI development building production data models in a modern cloud data stack
• Advanced SQL proficiency (e.g., complex joins, window functions, query optimization) with a track record of performance tuning in Snowflake, BigQuery, or Redshift
• 2+ years implementing and maintaining dbt projects (models, tests, macros, documentation) in production with Git-based workflows
• Hands-on experience orchestrating ELT/ETL pipelines with Airflow, Dagster, or similar, including scheduling, dependency management, and alerting
• Experience building semantic layers and BI models (e.g., Looker/LookML, Semantic Layer, or equivalent) to enable reliable self-serve analytics
• Demonstrated use of automated data quality testing and data observability (e.g., dbt tests, Great Expectations, or similar) and ownership of documentation and lineage
• Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field, or equivalent practical experience
• Proven success partnering cross-functionally with Product, Marketing, and Engineering to translate ambiguous requirements into scalable datasets and clear deliverables

Desirable

• Instacart’s Lifecycle Efficiency team sits within Data Science & Analytics and partners closely with Incentives Marketing to power how we invest, measure, and optimize customer growth
• We’re looking for an Analytics Engineer to build and own the data models, pipelines, and semantic layers that enable clear, trusted measurement of a marketing portfolio with approximately millions in annual spend
• In this role, you will shape the data foundation for incentives, promotions, and lifecycle communications across our marketplace
• You’ll collaborate daily with Data Engineering, Product, Marketing, and Engineering to define source-of-truth datasets, standardize KPI definitions, and enable self-serve analytics at scale
• Your work will unlock faster decision-making, measurable ROI, and smarter experimentation
• You’ll join a focused team of 5 that values collaboration, clear thinking, and rolling up our sleeves to solve complex problems
• Design, build, and maintain robust, production-grade data models (e.g., in dbt) that power incentives, promotions, and lifecycle analytics, including standardized fact/dimension tables and a consistent metrics layer
• Partner with Data Engineering to model source data from multiple systems (e.g., marketing platforms, event streams, transactional data) and implement efficient, auditable ELT patterns in a modern cloud warehouse
• Define and operationalize KPI and metric definitions for marketing efficiency and ROI; enable self-serve analytics in BI tools by implementing clean, documented semantic models and LookML (or equivalent)
• Set and enforce data quality standards with automated testing, lineage, documentation, and monitoring to ensure stakeholders can trust dashboards and analyses used to manage millions in annual spend
• Collaborate with Product, Marketing, and Engineering to scope requirements, prioritize a roadmap, and deliver high-impact datasets for experimentation, attribution, cohorting, and lifecycle performance reporting
• Continuously improve performance, reliability, and cost efficiency of pipelines and queries; drive best practices in version control, code review, and CI/CD for analytics engineering