Software Engineer, Data Infrastructure

Figma is growing our team of passionate creatives and builders on a mission to make design accessible to all. Figma’s platform helps teams bring ideas to life—whether you're brainstorming, creating a prototype, translating designs into code, or iterating with AI. From idea to product, Figma empowers teams to streamline workflows, move faster, and work together in real time from anywhere in the world. If you're excited to shape the future of design and collaboration, join us! The Data Platform team at Figma builds and operates the foundational systems that power analytics, AI/ML, 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 platforms such as the Snowflake data warehouse, ML Datalake, orchestration and pipeline infrastructure, and large-scale data ingestion and processing systems, 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 data infrastructure layer for Figma's AI-powered products, driving cost and performance optimizations across our data stack, scaling our ingestion and reverse ETL capabilities for new product use cases, and strengthening data quality, reliability, and compliance at every layer. If you're passionate about building scalable, high-performance data platforms that empower teams across Figma, we'd love to hear from you! This is a full-time role that can be held from one of our US hubs or remotely in the United States. What you'll do at Figma: 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. We’d love to hear from you if you have: 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. While it’s not required, it’s an added plus if you also have: 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