Senior/Staff Analytics Engineer

Render is building a modern cloud platform for developers creating AI-native, full-stack, multi-service applications. The Senior/Staff Analytics Engineer will lead the evolution of Render’s Analytics Engineering platform by building trusted, reusable data models, establishing engineering standards, enabling self-service and AI-assisted analytics, and improving data reliability and efficiency. The role partners with analysts, scientists, data engineers, and business teams while setting the technical direction for Analytics Engineering.

Responsibilities:

• Own our Analytics Engineering architecture. Guide the long-term evolution of Render’s Analytics Engineering platform, strengthening foundational models so our data remains trusted, scalable, and ready for AI-assisted analytics
• Build trusted, reusable data models. Design, build, and maintain governed dbt models that serve as authoritative sources for business-critical metrics across Product, Engineering, Growth, Go-to-Market, Finance, and other teams
• Establish engineering standards. Define and improve practices for data modeling, testing, documentation, version control, CI/CD, and code review. Review high-impact dbt changes and reduce technical debt across the analytics codebase
• Enable self-service and AI-assisted analytics. Design and maintain semantic and context layers that make metrics consistent, data discoverable, and analysis reliable for both people and AI tools
• Partner closely with analysts and scientists. Help analysts and scientists translate domain-specific business logic into scalable, maintainable warehouse models, moving shared logic out of individual reports and dashboards and into governed datasets
• Improve reliability and efficiency. Partner with Data Engineering to strengthen source data quality, warehouse architecture, and BigQuery performance and cost efficiency
• Raise the bar for Analytics Engineering. Mentor analysts, scientists, and analytics engineers on dbt development, dimensional modeling, and engineering best practices, and help teams adopt shared standards

Requirements:

• 7+ years of experience in Analytics Engineering, Data Engineering, or a related technical field, including at least 3 years of hands-on experience running dbt in production
• Expert-level SQL skills and a track record of designing and owning scalable Kimball dimensional models
• Deep expertise in modern Analytics Engineering practices, including Git, testing, CI/CD, documentation, data contracts, lineage, and governance
• Experience building semantic and context layers, trusted metrics, and warehouse architectures that support self-service analytics for humans and AI agents across multiple business domains
• Strong understanding of modern cloud data warehouse architecture, performance optimization, and cost efficiency, with experience in platforms such as BigQuery, Databricks, or Snowflake
• A track record of establishing technical standards, influencing engineering practices without formal authority, and improving reliability while reducing technical debt
• Proven ability to partner closely with analysts, scientists, and business stakeholders to translate requirements into scalable, maintainable data models
• Excellent communication skills
• You can explain complex technical concepts and tradeoffs to technical and non-technical audiences and build alignment across teams
• Experience building data models that connect product usage, acquisition, CRM, and financial data across the customer lifecycle
• Experience using AI-assisted coding or analytics tools such as Codex, Cursor, or Claude Code to accelerate data modeling, testing, documentation, or exploration
• Hands-on experience with Render's analytics stack, including BigQuery, Segment, dbt, and analytics tools such as Metabase or Mixpanel
• Experience with developer-focused, product-led, sales-led, or usage-based businesses
• Knowledge of cloud infrastructure, PaaS, developer tooling, or other technically complex products