About this role
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's Data Engineering organization builds and operates the data platforms that power every Pinterest product — the batch and streaming pipelines that produce training data for our ML models, the storage and table formats that back our data lake, the workflow orchestration that runs it all, and the analytics platforms that fuel experimentation and decision-making. We're in the middle of a multi-year modernization effort: moving to streaming-first ingestion (CDC, Kafka, Flink), open table formats (Iceberg), a consolidated workflow platform, and retiring legacy footprints along the way. We work closely with ML, product, and analytics teams to make Pinterest's data platforms faster, more reliable, and more cost-efficient.
What You'll Do
• Own the strategy and execution of a multi-year portfolio of data platform modernization programs — spanning ingestion (CDC/Kafka/Flink), table format migrations (Hive to Iceberg), workflow consolidation, and legacy system deprecation — in partnership with engineering leaders across Data Engineering.
• Lead migrations across multiple orgs involving many producer and consumer teams: anticipate technical failure points early, resolve core technical uncertainties with engineering, and settle cross-org priority conflicts.
• Define the portfolio's metrics, governance, and reporting mechanisms so engineering, Platforms leadership, and Finance have a clear, current view of progress, validated savings, risk, and decisions.
• Own a large data infrastructure efficiency portfolio end to end, from savings strategy and forecasting to validation with team-level DRIs, Finance, and InfraGov.
• Influence senior leadership (up to VP/e-staff) with clear recommendations on technical tradeoffs, investment, and sequencing, and drive consensus across orgs.
• Mentor TPMs and engineering DRIs, and cr