Staff Software Engineer : Storage, Search, & Data Platforms

About this role

The Storage, Search, and Data (SSD) group is the custodian of Uber's digital intelligence. We provide the unified data fabric-from transactional persistence to real-time discovery-that powers every ride, delivery, and autonomous decision made across the planet.


As a Staff Engineer, you will operate as a primary technical architect for the SSD organization. You will be responsible for the technical vision that bridges our on-premise footprint with a Modern, Cloud-Native Data Ecosystem. This role requires a rare blend of deep distributed systems expertise, leading from the front, and the strategic influence to align multiple platform teams toward a unified "Data-to-AI" future.


What You Will Lead

• Strategic Evolution: Define and execute the multi-year roadmap to transition Uber from "Data Storage" to a Cloud-Native Data Provider, solving for cross-region latency, global metadata consistency, and exabyte-scale cost efficiency.
• The AI-Data Convergence: Partner with Uber's AI/ML leadership to architect the "Data-to-GPU" pipeline. You will design the one-stop storage APIs that allow researchers to leverage high-performance data access across multi-cloud regions and vendors seamlessly.
• Platform Innovation: Drive the next generation of our core engines: Docstore (NoSQL), Vitess (Sharded MySQL), Apache Pinot (Real-time Analytics), and OpenSearch (Discovery).
• You will represent Uber in the global community as a leader in key open source technologies including Apache, Hudi, Iceberg and many others

- Basic Qualifications -

• Industry Leadership: 12+ years of software engineering experience, with a proven history of designing and operating massive-scale distributed data systems.
• Systems Mastery: Elite engineering skills in Go, Java, C++, or Rust. You are comfortable deep-diving into database internals, kernel-level optimizations, and complex distributed consensus protocols.
• Strategic Execution: Proven experience leading technical strategy across ...