Data Retrieval Engineer

Job-:
Research and Advance Engineering

Location:

Redford, MI, United States Workplace:
Hybrid

Schedule:

Full time Posted:
Closes:
We made history and now we work to transform the future – for our customers, our communities and our families. You'll see your work on the road every day, helping people move freely and pursue their dreams. At Ford, you can build more than vehicles. Come build what matters.
    Shape the Future of Manufacturing with ATPAt Ford, we aren’t just building vehicles — we are redefining how the world moves. Within the Advanced Industrial Technology and Platforms (ATP) organization, we are the architects of Ford’s next industrial revolution.

Our mission is clear: we build and scale industrial platforms, technologies, and capabilities that unlock Ford Firsts to drive sustained competitive advantage in manufacturing. Driven by our vision to "build once, scale everywhere," we bridge the gap between cutting-edge technology and global physical deployment.

Our guiding mantra is simple yet relentless:
Scale what matters, win what's next.

If you are a high-tech builder, thinker, or innovator who thrives at the intersection of software, hardware, and massive scale, ATP is where you belong. Join us, and write the next chapter of industrial  this position...Ford is seeking a hands-on engineer to make enterprise data fast to connect, fast to retrieve, and fast to bring online. This role focuses on building the algorithms, architecture, and platform capabilities that connect data across domains, represent relationships effectively, and deliver data with predictable performance ’ll work with diverse and evolving data sources, choosing representations and retrieval strategies that fit their structure and use.

A key measure of success is reducing the time and effort required to connect a new source and make its data discoverable and usable—without creating fragile, one-off integrations. You’ll benchmark your choices against real workloads and make performance, connectivity, and onboarding speed explicit engineering priorities.

Qualifications You'll have...Bachelor's degree in a related field, or equivalent practical experience (5-7+ years)
Professional experience building and operating data platforms, data infrastructure, or backend systems in production.

Strong programming skills in at least one systems or data language such as Python, Go, or Rust, with a track record of shipping maintainable, tested code.

Experience building reusable connectors, ingestion frameworks, or self-service source-onboarding capabilities. This includes designing software that non-platform users operate directly to bring their own sources online.

Production experience with graph databases, graph processing frameworks, or graph query languages. Examples include Spanner Graph, Neo4j, Tiger Graph, GQL, Cypher, Gremlin, or SPARQL.

Experience with relational and columnar systems, document or key-value stores, search indexes, time-series platforms, or vector retrieval systems. Examples include Big Query, Spanner, AlloyDB, Cloud SQL, Bigtable, Firestore, Milvus, or Vertex AI Vector Search.

Knowledge of indexing approaches such as adjacency indexes, B-trees, LSM trees, inverted indexes, bitmap indexes, or approximate nearest-neighbor indexes. You can explain when each is the right choice and have used profiling and benchmarking to drive measurable performance improvements.

Experience with metadata catalogs, schema registries, lineage, data discovery, or federated data-product platforms. Examples include Big Query Analytics Hub.

Familiarity with cloud infrastructure, Kubernetes, observability platforms, and infrastructure-as-code. Hands-on experience with Google Cloud Platform, including GKE, IAM, Cloud Monitoring and…