Senior/Staff Analytics Engineer
ABOUT THE ROLE
We're looking for a Senior/Staff Analytics Engineer to join our growing Data team and lead the evolution of our Analytics Engineering platform. As Render scales, you'll build the foundations that turn raw data into trusted, reusable models for analytics, executive reporting, and AI-assisted decision-making.
In this role, you'll partner closely with analysts, scientists, data engineers, and teams across Product, Engineering, Growth, Go-to-Market, Finance, and other business functions. Analysts and scientists will bring domain expertise and own business logic within their areas; you'll own the technical architecture, modeling standards, and engineering practices that help that work scale reliably.
As a Senior/Staff-level individual contributor, you'll set the technical direction for Analytics Engineering at Render—strengthening data quality, improving the developer experience, and building semantic and context layers that make data easier to discover and use. You'll help teams move faster with consistent metrics and maintainable models they can trust.
WHAT YOU'LL DO
- 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.
WHAT WE'RE LOOKING FOR
- 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.
NICE-TO-HAVES
- 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.