Uber · San Francisco, CA, United States
Skills: Python, Java, Rust
Staff Software Engineer – Streaming Data Platform
About the role and team
Leading at Uber means setting technical direction and building foundational systems that operate at global scale. As a Staff Software Engineer on the Streaming Data Platform team, you will shape the architecture and evolution of the critical infrastructure that powers real-time data movement and processing across Uber's global marketplace.
Our streaming data platform serves as the backbone for real-time applications, analytics, and machine learning, enabling engineering teams to reliably produce, process, and consume massive volumes of data. The platform supports a diverse range of mission-critical workloads, where scalability, low latency, high availability, and operational efficiency are essential.
You will tackle some of the most challenging problems in large-scale distributed systems, from designing next-generation streaming architectures and improving fault tolerance to optimizing resource utilization and simplifying platform operations. Your work will directly influence the reliability, efficiency, and developer experience of foundational infrastructure used across Uber.
This role is for an accomplished technical leader who can navigate complex architectural tradeoffs, anticipate future scaling challenges, and drive transformative engineering initiatives across organizational boundaries. You will combine deep technical expertise with strategic thinking, influence long-term technology investments, and empower other engineers to deliver impactful solutions.
If you are passionate about solving hard distributed systems problems, pushing the boundaries of infrastructure performance, and building platforms that enable innovation at unprecedented scale, this is where you will make your mark.
What you'll do
Define and drive the technical vision and architecture for Uber's streaming data platform, ensuring its long-term scalability, reliability, performance, and extensibility a …truncated… short-term del…
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