Staff Data Platform Engineer at Shield AI

Department: Hivemind Enterprise Division

Compensation

$150,000 – $230,000/yr

Setup: On-site
Location: San Diego, California
Type: Full-time
Level: staff+
Posted: Aug 26, 2026

Description

Shield AI is hiring a Staff Data Platform Engineer to define and build the data foundation for its AI Factory. The role leads the architecture and implementation of a knowledge-graph-centered API layer, data operations infrastructure, schema and lineage patterns, agent data access, integrations, developer tools, and technical standards. It requires hands-on technical leadership across distributed data systems, storage and compute technologies, Go and Python production systems, Kubernetes, cloud or customer-managed environments, and collaboration with autonomy, ML, test, infrastructure, product, and customer-facing teams.

Required Skills

Knowledge graphs
API design
Data modeling
Schema evolution
Kubernetes
Go
Python
Data lifecycle
Observability
Infrastructure as Code

Additional Information

Required Previous Experiences: Significant experience designing and operating distributed data solutions, storage systems, or data-intensive backend services
Experience delivering production systems in Go and Python
Experience across relational, graph, object, analytical, columnar, and file storage systems
Experience designing reliable ingestion and access paths for high-volume data
Experience deploying data infrastructure in cloud or customer-managed environments using Infrastructure as Code and platform engineering practices
Experience defining architecture and technical standards while remaining hands-on in implementation and debugging
Experience collaborating with ML researchers, autonomy engineers, test teams, platform engineers, and product stakeholders
Preferred Qualifications: Experience with specialized modern databases, including graph and OLAP systems
Experience with graph-backed retrieval, agent tooling, structured RAG, provenance-aware context, or explainable retrieval systems
Experience with S3-compatible APIs, cloud object storage, content-addressable storage, multipart transfer, or large-file lifecycle management
Experience with Apache Arrow, Parquet, columnar formats, time-series data, or high-performance analytical query systems
Experience with OpenAPI, AsyncAPI, WebSockets, generated SDKs, and long-lived public API contracts
Experience with Kubernetes storage and data operators, Terraform, Helm, GitOps, and repeatable platform distribution
Experience with Ray and connecting workflow execution to data lineage and artifact management
Experience with Kafka, NATS, Redpanda, or comparable event-driven streaming and ingestion technologies
Experience with data and analysis in robotics or AI domains
Experience with ML data lifecycle systems, experiment tracking, dataset management, evaluation infrastructure, feature or artifact stores, and model versioning
Experience with observability tools, distributed tracing, and benchmarking
Experience with data security, authorization, governance, retention, classification, and auditability across shared platforms