Figma
Data Platform Engineer
About this role
Figma is seeking a Data Platform Engineer to design and operate foundational systems powering analytics, data science, and AI capabilities. You'll build scalable infrastructure enabling self-serve data tools, improve developer workflows, and ensure reliability across the company's data ecosystem.
What you'll do
- Build and maintain shared data platform serving analytics, data science, and product development teams
- Enhance developer experience through improved local development, testing, CI/CD, and deployment workflows
- Develop self-service tools enabling teams to build, deploy, and operate reliable data pipelines
- Increase system reliability via monitoring, data quality checks, incident response, and performance optimization
- Establish data governance by defining ownership, lineage, and quality standards
- Collaborate across teams to define supported patterns for batch, micro-batch, reverse ETL, and product delivery
What they're looking for
- Production data platform design and operation
- Python or similar software engineering language
- Distributed systems and scalable architecture
- Cloud data warehouses and transformation frameworks
- Workflow orchestration and infrastructure-as-code
- Observability and monitoring tools
- Developer infrastructure and CI/CD optimization
- Cross-functional project leadership
Benefits
- Equity compensation
- Health, dental, and vision coverage
- Retirement benefits with company contributions
- Parental leave and family planning support
- Mental health and wellness benefits
- Flexible paid time off and paid sick leave
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Figma
Figma is a design platform that leverages AI and machine learning to power intelligent features like search, ranking, and generative capabilities. The company is hiring software engineers, ML engineers, and support specialists to build scalable infrastructure, AI-powered automations, and support systems that serve millions of users.
- Website
- figma.com
Likely interview questions
- Describe your experience building or operating a production data platform—what were the key architectural decisions you made?
- How have you improved developer workflows in data engineering or infrastructure roles, and what impact did those changes have?