Software Engineer - Data Infrastructure

The shape of it

In the posting’s own words

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!

What it asks for · 5

• 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.

Also a plus

• 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.

What the job covers

• 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.

Tools and skills named

Words the posting leans on

• data23×
• platforms8×
• experience7×
• infrastructure7×
• systems6×
• engineers5×
• ingestion5×
• product5×
• technical5×
• building4×
• business4×
• design4×
• processing4×
• build3×
• compliance3×
• data infrastructure3×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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