Software Engineer, Figma

• 6+ years of experience 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
• A proven track record of impact-driven problem-solving in a fast-paced environment
• A strong sense of engineering excellence, with a focus on high-quality, reliable, and performant systems
• Excellent technical communication skills, with experience working across both technical and non-technical counterparts
• The ability to navigate ambiguity, take ownership, and drive projects from inception to execution
• Experience mentoring and supporting engineers, fostering a culture of learning and technical excellence

Desirable

• The Data Infrastructure team at Figma builds and operates the foundational platforms that power analytics, AI, and data-driven decision-making across the company.
• We serve a diverse set of stakeholders, including AI Researchers, Machine Learning Engineers, Data Scientists, Product Engineers, and business teams that rely on data for insights and strategy.
• Our team owns and scales critical data platforms such as the Snowflake data warehouse, ML Datalake, and large-scale data movement and processing applications, managing all data flowing into and out of these platforms
• Despite being a small team, we take on high-scale, high-impact challenges. In the coming years, we’re focused on building the foundational infrastructure to support AI-powered products, developing streaming interconnects between our core systems, and revamping our orchestration and financial data architecture with a strong emphasis on data quality, reliability, and efficiency.
• Design and build large-scale distributed data systems that power analytics, AI/ML, and business intelligence
• Develop batch and streaming solutions to ensure data is reliable, efficient, and scalable across the company
• Manage data ingestion, movement, and processing through core platforms like Snowflake, our ML Datalake, and real-time streaming systems
• Improve data reliability, consistency, and performance, ensuring high-quality data for engineering, research, and business stakeholders
• 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