Staff Engineer, Data Platform

Responsibilities

• Architect and implement a knowledge graph and multimodal Graph API for human, service, and agentic workflows.
• Research, optimize, and maintain storage, indexing, query, ingestion, and compute infrastructure across the data lifecycle.
• Establish patterns for schema modeling, relationships, lineage, schema evolution, observability, security, reliability, disaster recovery, and lifecycle management.
• Build agent-access APIs for structured, connected, explainable context and develop reference architectures, deployment patterns, benchmarks, and operational guidance.
• Partner with autonomy, ML, test, infrastructure, product, and customer-facing teams to turn workflows into reusable platform capabilities.
• Deliver integrations for simulation, testing, training, and edge-device data; create self-service APIs, SDKs, tools, examples, and diagnostics.
• Evaluate emerging technologies and make build-versus-buy decisions while remaining hands-on in implementation and debugging.

Requirements

• Significant experience designing and operating distributed data solutions, storage systems, or data-intensive backend services.
• Strong production software engineering experience with languages such as Go and Python.
• Deep understanding of data modeling, API design, schema evolution, identity, consistency, indexing, query planning, and data lifecycle management.
• Experience with 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.
• Experience deploying data infrastructure in cloud or customer-managed environments using Infrastructure as Code and platform engineering practices.
• Ability to evaluate technologies through prototypes, benchmarks, operational requirements, and lifecycle cost.
• Experience defining architecture and technical standards while implementing and debugging production systems.
• Ability to collaborate across ML, autonomy, test, platform, and product teams and communicate complex architecture clearly.

Nice-to-haves

• Graph, OLAP, or other specialized modern databases; graph-backed retrieval; structured RAG; agent tooling; provenance-aware or explainable retrieval.
• S3-compatible APIs, cloud object storage, content-addressable storage, multipart transfer, and large-file lifecycle management.
• Apache Arrow, Parquet, columnar formats, time-series data, and high-performance analytical query systems.
• OpenAPI, AsyncAPI, WebSockets, generated SDKs, and long-lived public API contracts.
• Kubernetes storage and data operators, Terraform, Helm, GitOps, and repeatable platform distribution.
• Ray or other distributed execution technologies integrated with data lineage and artifact management.
• Kafka, NATS, Redpanda, or comparable event-driven ingestion systems.
• Robotics or AI data systems; ML data lifecycle systems; experiment tracking; dataset management; evaluation infrastructure; feature or artifact stores; model versioning.
• Observability, distributed tracing, benchmarking, data security, authorization, governance, retention, classification, and auditability.