Senior Staff Software Engineer: Data & Storage Platform

As a Senior Staff Engineer , you will be the architect behind Uber's Elastic Compute , Unified Semantic Layer , and Self-Evolving Knowledge Platform . You will lead the transition from legacy on-premise systems to a Modern, Cloud-Native Ecosystem , designing solutions that handle exabytes of data. Whether it's optimizing Transactional Distributed Storage , or operationalizing Agentic Data Intelligence , your work will define the industry standard for high-performance infrastructure.

What You Will Do

• Architect the Multi-Modal Fabric: Unify batch, streaming, and AI compute into one intelligent fabric, enabling real-time insights and trustworthy AI agents at a global scale.
• Revolutionize Storage & Catalog: Drive the architecture for a unified catalog and metadata management service for unstructured data, leveraging native cloud object store capabilities.
• Operationalize AI Intelligence: Partner with teams like QueryCopilot and DataIQ to bridge human validation with autonomous reasoning through agentic workflows.
• Lead Storage Modernization: Evolve our massive-scale persistence layers-including Docstore (Transactional Distributed Storage) and Distributed MySQL-to increase resiliency and reduce operational overhead.
• Open Source & Act as a force multiplier by contributing to the community (Hudi, Iceberg, Presto).

• 14+ Years of Engineering Excellence: Proven experience designing and operating world-class distributed data and storage systems.
• Mastery of Storage Internals: Extensive storage experience is a must. You have deep expertise in:

• Batch & Object Storage: HDFS, Cloud Object Storage (S3/GCS/OCI), and Blobstore metadata management.
• Storage Optimization: Practical experience with Apache Hudi or Apache Iceberg for lakehouse architectures.
• Transactional Systems: Experience with distributed transactional storage (e.g., Docstore, Google Spanner, TiDB).
• NoSQL & Cache: Cassandra, Redis, and high-throughput Key-Value stores.

• Data + AI Convergence: Deep understanding of how compute fabrics (Spark, Flink, Ray) integrate with vector databases and model-serving platforms.
• Query Engine Proficiency: Architect-level knowledge of Presto, Trino, or Hive for large-scale analytical processing.
• Systems Programming: Expert-level command of Java, Go, Scala, or C++ with a focus on performance tuning and distributed consensus., * Infrastructure at Scale: Experience designing AI infrastructure, including RAG (Retrieval-Augmented Generation) systems and high-bandwidth data loading for GPUs.
• Distributed Relational Mastery: Hands-on experience with Sharded/Distributed MySQL (Vitess) and managing large-scale tabular data.
• Multi-Cloud Vision: Demonstrated ability to build portable data solutions across OCI and GCP, optimizing for resource efficiency and intelligent scheduling.
• SLA-Driven Reliability: Expertise in building observability, data freshness, and quality frameworks for Tier-0 mission-critical services.Strategic Influence: Proven ability to lead platform modernization, mentor Staff-level engineers, and influence long-term technical strategy across multiple organizations.

Uber's Data Platform is the heart of the company's critical decision-making and AI-driven intelligence. We are evolving from traditional data processing toward a Next-Generation Data Intelligence Platform -a unified, elastic fabric that collapses the silos between batch, streaming, and multimodal data.