Data Retrieval Engineer

Ford Motor Company • Redford Charter Township, MI

10h • $85,400 - $192,900 • Hybrid

About The Position

Ford Motor Company is seeking a hands-on engineer in its Advanced Industrial Technology and Platforms organization to make enterprise data easier and faster to connect, retrieve, and bring online. The role builds algorithms, architecture, and platform capabilities for connecting data across domains, representing relationships, and delivering predictable performance at scale. It focuses on reusable integrations, measurable onboarding speed, and retrieval strategies benchmarked against real workloads.

The engineer will build connectivity and ingestion capabilities, model cross-domain relationships, optimize indexing and query execution, and improve performance across the complete path from source connection through retrieval. The position also integrates discovery, metadata, lineage, governance, quality, and access controls into the platform. It includes operating services on Google Cloud, using AI-assisted engineering and connector creation with validation and governance, and building retrieval capabilities for AI consumers. The role provides technical leadership and helps shape the roadmap with data producers, analytics teams, and AI/ML teams.

The position is hybrid and requires onsite work four or more days per week. Salary grade is determined by skills and experience, with stated ranges of $85,400–$143,200 for grade 6, $99,600–$166,600 for grade 7, and $115,000–$192,900 for grade 8. Visa sponsorship is available.

Requirements

• Bachelor's degree in a related field or equivalent practical experience; the posting indicates 5–7+ years.
• Professional experience building and operating production data platforms, data infrastructure, or backend systems.
• Strong programming skills in at least one systems or data language such as Python, Go, or Rust, with a record of shipping maintainable, tested code.
• Experience building reusable connectors, ingestion frameworks, or self-service source-onboarding capabilities for direct use by non-platform users.
• Production experience with graph databases, graph processing frameworks, or graph query languages such as Spanner Graph, Neo4j, TigerGraph, 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 BigQuery, Spanner, AlloyDB, Cloud SQL, Bigtable, Firestore, Milvus, and Vertex AI Vector Search.
• Knowledge of indexing approaches such as adjacency, B-tree, LSM-tree, inverted, bitmap, and approximate nearest-neighbor indexes, and experience using profiling and benchmarking to achieve measurable performance improvements.
• Experience with metadata catalogs, schema registries, lineage, data discovery, or federated data-product platforms.
• Familiarity with cloud infrastructure, Kubernetes, observability platforms, and infrastructure-as-code; hands-on Google Cloud Platform experience, including GKE, IAM, Cloud Monitoring and Logging, and Terraform, is expected.
• Experience in automotive, manufacturing, engineering, supply chain, or another complex, data-intensive environment.
• Practical experience applying AI coding assistants and agentic tools to specific engineering workflow tasks, with the ability to verify their output.

Nice To Haves

• Master's degree in Computer Science, Data Engineering, or a related field.
• Experience with streaming and change-data-capture pipelines such as Pub/Sub, Dataflow, Datastream, Kafka, or Flink for near-real-time ingestion.
• Experience with industrial data sources and protocols such as OPC UA, MQTT, historians, or MES and PLM systems.
• Experience building retrieval infrastructure for AI/ML workloads on Vertex AI, including embedding pipelines, vector search, hybrid search, or retrieval-augmented generation.
• Experience applying large language models to data engineering tasks such as schema inference, entity resolution, data mapping, or automated data quality checks.
• Experience shipping AI-powered developer or data tooling to end users, with human review, sandboxing, and guardrails for safe deployment of generated code.
• Experience with agent frameworks and tool-use protocols such as Model Context Protocol, or exposing data platforms as tools for AI agents.
• Experience designing evaluation frameworks for LLM-based systems, including retrieval quality metrics and regression testing.
• Experience with query engines and open table formats such as BigQuery, BigLake, Dataproc and Spark, Trino, or Apache Iceberg.
• Experience designing data products or APIs consumed by multiple teams across a large enterprise.
• Strong written and verbal communication skills, including the ability to explain architectural trade-offs to non-technical stakeholders.

Responsibilities

• Design reusable connectors, ingestion frameworks, and self-service onboarding tools so sources can be registered, profiled, and queried through configuration rather than bespoke code.
• Design graph and relational schemas linking parts, plants, processes, suppliers, equipment, and quality events to support cross-domain queries.
• Select and implement indexing strategies and query execution paths that meet latency and throughput targets as data volumes grow.
• Profile, benchmark, and tune performance across source connection, ingestion, indexing, discovery, and retrieval.
• Integrate metadata catalogs, schema registries, and lineage tracking so connected sources are findable, documented, and traceable.
• Embed data quality checks, access control, governance policies, and lineage capture into ingestion and retrieval.
• Define service-level objectives for retrieval latency, freshness, and onboarding time, and build observability using Cloud Monitoring and Cloud Logging.
• Deploy on Google Kubernetes Engine and Cloud Run using Terraform, support the production platform, and resolve root causes.
• Design capabilities that can be scaled globally from an initial plant deployment without re-engineering.
• Use AI coding assistants and large language models for appropriate repetitive engineering tasks, with validation and engineer review.
• Build self-service, AI-driven connector creation experiences, APIs, and agent workflows with governed deployment guardrails.
• Build AI retrieval capabilities, including Vertex AI embedding pipelines, hybrid search across Vertex AI Vector Search and BigQuery, and graph-aware retrieval.
• Create evaluation harnesses and benchmarks for AI-assisted onboarding and retrieval quality, measuring precision, recall, and latency against real workloads.
• Set technical direction for connectivity and retrieval, author design documents, lead code and design reviews, and uphold performance and reliability standards.
• Partner with data producers, analytics teams, and AI/ML teams to understand access patterns and translate them into platform priorities.

Benefits

• Immediate medical, dental, vision, and prescription drug coverage.
• Flexible family care days, paid parental leave, new parent ramp-up programs, and subsidized backup childcare.
• Family-building benefits, including adoption and surrogacy expense reimbursement and fertility treatments.
• Vehicle discount program for employees and family members, and management leases.
• Tuition assistance.
• Employee resource groups.
• Paid time off for individual and team community service.
• Paid holidays, including the week between Christmas and New Year's Day.
• Paid time off and the option to purchase additional vacation time.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior