Software Engineer - Data Infrastructure

Figma is hiring a Software Engineer for its Data Platform team. The role builds and operates scalable data infrastructure for analytics, AI/ML, and business intelligence, including data processing, ingestion, orchestration, and storage platforms.

Responsibilities:

• Design and build large-scale distributed data systems that power analytics, AI/ML, and business intelligence across Figma
• Develop batch and streaming solutions to ensure data is reliable, efficient, and scalable across the company
• Manage and evolve core platforms like Snowflake, our ML Datalake, orchestration infrastructure, and real-time ingestion systems
• Improve data reliability, consistency, and compliance, ensuring high-quality data for engineering, research, and business stakeholders
• Identify and drive cost optimization opportunities across data processing, compute infrastructure, and storage
• Collaborate with AI researchers, data scientists, product engineers, and business teams to understand data needs and build scalable solutions
• Drive technical decisions and best practices for data ingestion, orchestration, processing, and storage
• Mentor and support engineers, fostering a culture of learning and technical excellence

Requirements:

• 5+ years of backend or infrastructure engineering experience, including designing and building distributed data infrastructure at scale
• Strong expertise in batch and streaming data processing technologies such as Spark, Flink, Kafka, or Airflow/Dagster
• Proven track record of impact-driven problem-solving in fast-paced environments, with a strong focus on high-quality, reliable, and performant systems
• Excellent technical communication skills, with experience collaborating across both technical and non-technical stakeholders
• Experience mentoring engineers and fostering a culture of learning and technical excellence
• Familiarity with our stack, including Golang, Python, SQL, frameworks such as dbt, and technologies like Spark, Kafka, Snowflake, and Dagster
• Experience building data infrastructure for AI/ML pipelines, including model serving, feature stores, or dataset compliance
• Experience with reverse ETL, personalization platforms, or real-time event ingestion systems
• Experience with data governance, access control, and cost optimization strategies for large-scale data platforms
• The ability to navigate ambiguity, take ownership, and drive projects from inception to execution