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Staff Software Engineer, Infrastructure Data Engineering

Pinterest · New York, NY, US; Remote, US · Remoto

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 . Pinterest is seeking a Staff Software Engineer to build the data systems that help teams understand and manage infrastructure usage and cost. You’ll lead the design and hands-on delivery of reliable data products, including pipelines, models, and services, that support infrastructure planning, cost attribution, and budget workflows. In this individual-contributor role, you’ll shape technical direction, navigate complex cross-team work, and make infrastructure data more accurate, consistent, and useful. What you’ll do: Set technical direction and help define a roadmap for reliable, scalable infrastructure data products Design, build, and operate production ingestion and transformation pipelines, data models, and services for infrastructure usage, cost, ownership, and planning Strengthen shared data products with clear contracts, automated quality checks, reconciliation, documentation, and operational monitoring Lead ambiguous, cross-functional initiatives from problem definition and technical trade-offs through rollout and adoption; raise the engineering bar through mentorship and design reviews Leverage AI to scale the impact of yourself and the team, including: Accelerate investigation of data-quality or pipeline issues while validating findings against source data and production evidence Speed up implementation and documentation of

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