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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. 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.
Job Responsibility:
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)
Requirements:
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
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
Nice to have:
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
What we offer:
Eligible to participate in Uber's bonus program
May be offered an equity award & other types of comp
All full-time employees are eligible to participate in a 401(k) plan