Staff Software Engineer, Big Data Storage

About Pinterest:

Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.

Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible.

At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.

Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here.

We’re looking for a Staff Software Engineer to help build the next generation of Pinterest’s big data storage platform. You’ll work on some of the most exciting big data open source technologies — especially Apache Iceberg — at exabyte scale to power the data infrastructure that helps Pinners discover and do what they love.

As a Staff Software Engineer, you’ll serve as a technical leader and hands-on contributor, designing and building highly scalable storage systems for Pinterest’s data lake. You’ll partner closely with teams across data, ML/AI, analytics, and infrastructure to evolve our storage and metadata management capabilities, enabling efficient, reliable, and governed access to data at massive scale.

What you’ll do:

• Design, implement, and optimize Pinterest’s exabyte-scale data lake storage platform.
• Lead complex technical projects and initiatives for data lake storage and metadata management, driving them from architecture through execution.
• Collaborate with stakeholders and partner teams across the organization to design storage and metadata layer technologies that unlock big data and ML/AI innovations.
• Build storage capabilities that efficiently support large-scale ML/AI workloads, including high-throughput data access, schema evolution, and large-scale column backfills.
• Shape the long-term technical direction for scalable, reliable, and efficient big data storage systems.
• Engage with and contribute to open source communities such as Apache Iceberg, Spark, and Flink to