Role OverviewWe are looking for a Staff Data Platform Engineer to help define and build the data foundation of the AI Factory. This is a hands-on technical leadership role that requires balancing developer productivity, semantic clarity, operational reliability, system performance, portability, and long-term maintainability.
What You Will Do
Develop a unifying Graph API, own DataOps infrastructure, establish best-practices, turbocharge agentic data access, develop reference architectures, advise downstream teams, build first-party integrations, improve developer experience, drive technical direction, and raise operational quality.
Why It Might Be a Fit
This role offers the opportunity to shape both a core internal platform and a reference architecture delivered into demanding customer environments. Your work will determine how effectively engineers and agents can find trusted context, understand provenance, aggregate data from distributed systems, and turn operational experience into better intelligent systems.
Requirements
- Significant experience designing and operating distributed data solutions, storage systems, or data-intensive backend services
- Strong software engineering skills and a record of delivering production systems in languages such as Go and Python
- Deep understanding of data modeling, API design, schema evolution, identity, consistency, indexing, query planning, and data lifecycle concerns
- Experience working across multiple storage modalities, such as relational or graph databases, object storage, analytical or columnar systems, and file storage
- Experience designing reliable ingestion and access paths for high-volume or operationally important data
- Strong understanding of Kubernetes, Linux, networking, security, storage, observability, and distributed-systems fundamentals
- Experience deploying data infrastructure across cloud or customer-managed environments using modern Infrastructure as Code and platform engineering practices
- Ability to evaluate technologies through prototypes, benchmarks, operational requirements, and total lifecycle cost rather than feature lists alone
- Experience defining architecture and technical standards while remaining hands-on in implementation and debugging
- Demonstrated ability to collaborate with ML researchers, autonomy engineers, test teams, platform engineers, and product stakeholders
- Clear technical communication and the ability to make complex data architecture understandable to both specialists and downstream users
Benefits
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