Software Engineer, Data Infrastructure

Figma Figma · Enterprise · Canada +1 · Engineering

Figma's Data Platform team is seeking a Software Engineer to build and operate foundational systems for analytics, AI/ML, and data-driven decision-making. This role involves designing and developing large-scale distributed data systems, managing core platforms like Snowflake and ML Datalake, and improving data reliability and compliance. The engineer will collaborate with AI researchers and data scientists to build scalable solutions and drive technical decisions for data infrastructure.

What you'd actually do

  1. Design and build large-scale distributed data systems that power analytics, AI/ML, and business intelligence across Figma.
  2. Develop batch and streaming solutions to ensure data is reliable, efficient, and scalable across the company.
  3. Manage and evolve core platforms like Snowflake, our ML Datalake, orchestration infrastructure, and real-time ingestion systems.
  4. Improve data reliability, consistency, and compliance, ensuring high-quality data for engineering, research, and business stakeholders.
  5. Identify and drive cost optimization opportunities across data processing, compute infrastructure, and storage.

Skills

Required

  • 5+ years of backend or infrastructure engineering experience
  • designing and building distributed data infrastructure at scale
  • batch and streaming data processing technologies (Spark, Flink, Kafka, or Airflow/Dagster)
  • impact-driven problem-solving
  • high-quality, reliable, and performant systems
  • technical communication skills
  • collaborating across technical and non-technical stakeholders
  • mentoring engineers
  • fostering a culture of learning and technical excellence

Nice to have

  • Golang
  • Python
  • SQL
  • dbt
  • Spark
  • Kafka
  • Snowflake
  • Dagster
  • building data infrastructure for AI/ML pipelines
  • model serving
  • feature stores
  • dataset compliance
  • reverse ETL
  • personalization platforms
  • real-time event ingestion systems
  • data governance
  • access control
  • cost optimization strategies for large-scale data platforms
  • navigate ambiguity
  • take ownership
  • drive projects from inception to execution

What the JD emphasized

  • AI/ML
  • ML Datalake
  • data infrastructure
  • large-scale distributed data systems
  • data reliability
  • compliance

Other signals

  • building the data infrastructure layer for Figma's AI-powered products
  • ML Datalake
  • data ingestion and processing systems
  • data quality, reliability, and compliance