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Analytics Engineer, Lifecycle Efficiency
Location
United States
Posted
124 days ago
Salary
$138K - $174K / year
Seniority
Senior
Job Description
Analytics Engineer, Lifecycle Efficiency
Instacart
• 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.
Job Requirements
- 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.
Benefits
- Competitive compensation and benefits
- Flexible work arrangements
- New hire equity grant
- Annual refresh grants
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