Staff Data Engineer

Render is building a modern cloud platform for developers creating AI-native, full-stack, multi-service applications. The Staff Data Engineer will set the technical direction for data platform systems supporting analytics, data science, AI workflows, and operational decision-making, while building reliable pipelines, cloud services, infrastructure, and warehouse capabilities. The role also involves mentoring engineers and partnering cross-functionally to establish strong engineering standards.

Responsibilities:

• Own our Data Platform Engineering architecture. Set technical direction for core data systems, lead design decisions, and deliver reliable production systems. Partner with Data team leadership to shape the platform roadmap and evaluate architectural and build-versus-buy tradeoffs
• Build and operate trusted data pipelines. Develop ingestion services, integrations, and pipelines that deliver accurate, timely data. Make testing, logging, alerting, and observability part of how we build and operate the platform
• Evolve our orchestration platform. Own workflow execution, worker infrastructure, upgrades, and operational reliability. Improve deployment patterns and balance performance, cost, and maintenance needs as workloads grow
• Own warehouse reliability, performance, and access. Improve our BigQuery environment, including query performance, cost efficiency, permissions tooling, and governance. Partner with Analytics Engineering on warehouse architecture and the foundations that support scalable dbt models
• Build cloud services and infrastructure. Develop APIs, event-driven ingestion, and reusable services that connect our data systems. Use infrastructure as code to create maintainable infrastructure, reusable modules, and predictable deployments
• Establish engineering standards. Improve version control, CI/CD, testing, documentation, and operational practices across the Data Platform. Reduce technical debt and help the team make sound decisions about tooling and platform evolution
• Partner across teams. Work with analytics engineers, analysts, scientists, Engineering, IT, and business teams to clarify requirements, validate outputs, and communicate risks, tradeoffs, and timelines. Collaborate with IT and Security on secure access and data handling
• Support data science, analytics, and AI workflows. Prepare model-ready datasets, make features available to downstream systems, and help integrate AI and LLM workflows into production
• Grow the team’s technical capabilities. Mentor data engineers through code review, pairing, design feedback, and coaching, and help engineers take ownership of increasingly complex systems

Requirements:

• Demonstrated technical leadership as a tech lead or Staff-level engineer, with a track record of setting direction, driving architecture, and delivering complex data systems across a team
• Deep expertise in modern cloud data warehouses such as BigQuery, Databricks, or Snowflake, including data modeling, query performance, cost management, and access control
• Strong Python and SQL skills, with hands-on experience building reliable ingestion and ELT pipelines and maintainable data models
• Experience operating orchestration platforms such as Prefect, Airflow, or Dagster, including responsibility for deployments, upgrades, workers, and operational reliability
• Hands-on experience with cloud infrastructure and infrastructure as code, using Terraform or equivalent tools to build predictable, maintainable systems
• Experience building cloud applications or backend services, including APIs, ingestion services, and event-driven workflows
• Strong production engineering practices, including testing, logging, alerting, observability, version control, and CI/CD
• A track record of mentoring engineers through coaching, code review, pairing, and design feedback
• Excellent communication and cross-functional collaboration skills. You can explain technical tradeoffs, build alignment across data, engineering, and business teams, and lead through influence
• Experience using AI-assisted development tools such as Codex, Cursor, or Claude Code to accelerate delivery while maintaining a high bar for correctness and maintainability
• Hands-on experience with Render's data stack, including GCP, BigQuery, Segment, and dbt
• Experience with developer-focused, product-led, sales-led, or usage-based businesses
• Knowledge of cloud infrastructure, PaaS, developer tooling, or other technically complex products