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Render is a modern cloud platform for developers building AI-native, full-stack applications. The company has raised $260M in funding (Series C, February 2026) and serves over 7 million developers worldwide.
You'll join the Data team as a Senior/Staff Analytics Engineer to lead the evolution of Render's Analytics Engineering platform. This is a high-impact individual contributor role where you'll set technical direction and establish best practices across the organization.
Key responsibilities:
- Own the Analytics Engineering architecture and guide its long-term evolution, ensuring data remains trusted, scalable, and ready for AI-assisted analytics
- 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
- 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
- Design 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 to translate domain-specific business logic into scalable, maintainable warehouse models
- Work with Data Engineering to strengthen source data quality, warehouse architecture, and BigQuery performance and cost efficiency
- Mentor analysts, scientists, and analytics engineers on dbt development, dimensional modeling, and engineering best practices
You'll partner across Product, Engineering, Growth, Go-to-Market, Finance, and other business functions, translating requirements into scalable data models while establishing technical standards and influencing engineering practices without formal authority.
REQUIREMENTS:
- 7+ years of experience in Analytics Engineering, Data Engineering, or related technical field, including at least 3 years of hands-on production dbt experience
- Expert-level SQL skills and track record of designing and owning scalable Kimball dimensional models
- Deep expertise in modern Analytics Engineering practices: Git, testing, CI/CD, documentation, data contracts, lineage, and governance
- Experience building semantic and context layers, trusted metrics, and warehouse architectures supporting 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 (BigQuery, Databricks, or Snowflake)
- Track record of establishing technical standards, influencing engineering practices without formal authority, and improving reliability while reducing technical debt
- Proven ability to partner with analysts, scientists, and business stakeholders to translate requirements into scalable, maintainable data models
- Excellent communication skills; ability to explain complex technical concepts and tradeoffs to technical and non-technical audiences
- Experience building data models connecting product usage, acquisition, CRM, and financial data across the customer lifecycle
- Experience using AI-assisted coding or analytics tools (Codex, Cursor, Claude Code) to accelerate data modeling, testing, documentation, or exploration
NICE-TO-HAVES:
- Hands-on experience with Render's analytics stack (BigQuery, Segment, dbt, Metabase, Mixpanel)
- Experience with developer-focused, product-led, sales-led, or usage-based businesses
- Knowledge of cloud infrastructure, PaaS, developer tooling, or technically complex products
