Data Retrieval Engineer

Ford Motor Company

. Design and build reusable connectors, ingestion frameworks, and self-service onboarding tooling .

Posted 10/26/2026full-timeRedford • Michigan • United StatesMid-LevelSenior💰 $85,400 - $192,900 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building and operating data platforms and infrastructure, with strong programming skills in Python, Go, or Rust. Proficient in integrating metadata catalogs, schema registries, and implementing data quality checks within ingestion frameworks.

Highest-signal resume keywords

Data Platform DevelopmentGraph Database ExperienceCloud Infrastructure ProficiencyProgramming in Python, Go, or RustAI Coding Assistant Application

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Data Ingestion FrameworksGraph Processing FrameworksRelational and Columnar SystemsMetadata CatalogsSchema RegistriesIndexing StrategiesData Quality ChecksStreaming PipelinesChange-Data-Capture PipelinesVertex AI Retrieval Infrastructure

Tools & Technologies

Google Cloud PlatformGoogle Kubernetes EngineTerraformCloud MonitoringCloud Logging

Certifications & Qualifications

Bachelor's Degree in Related FieldMaster's Degree in Computer Science or Data Engineering (Advantageous)

Industry Keywords

AutomotiveManufacturingEngineeringSupply ChainData-Intensive Environment

Tech Stack

Tools & technologies

BigQueryCloudGoogle Cloud PlatformKubernetesNeo4jPythonRustTerraformGo

About the role

Key responsibilities & impact

Design and build reusable connectors, ingestion frameworks, and self-service onboarding tooling
Model relationships across parts, plants, processes, suppliers, equipment, and quality events using graph and relational schemas
Select and implement indexing strategies and query execution paths for defined latency and throughput targets
Profile, benchmark, and tune source connection, ingestion, indexing, discovery, and retrieval performance
Integrate metadata catalogs, schema registries, and lineage tracking
Embed data quality checks, access control, governance policy, and lineage capture into ingestion and retrieval
Define service-level objectives for retrieval latency, freshness, and onboarding time
Build observability with Cloud Monitoring and Cloud Logging
Deploy on Google Kubernetes Engine and Cloud Run using Terraform and support the platform in production
Design capabilities that can scale from one plant globally
Apply AI coding assistants and large language models to connector scaffolding, source profiling, schema mapping, and test development with validation and engineer review
Build self-service, AI-driven connector creation workflows and governed deployment guardrails
Design Vertex AI embedding pipelines, hybrid search across Vertex AI Vector Search and BigQuery, and graph-aware retrieval for AI consumers
Establish evaluation harnesses and benchmarks measuring precision, recall, and latency
Set technical direction, author design documents, lead code and design reviews, and shape the platform roadmap with data, analytics, and AI/ML teams

Requirements

What you’ll need

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 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
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
Knowledge of adjacency indexes, B-trees, LSM trees, inverted indexes, bitmap indexes, or approximate nearest-neighbor indexes
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 experience with Google Cloud Platform, including GKE, IAM, Cloud Monitoring and Logging, and Terraform
Experience in automotive, manufacturing, engineering, supply chain, or another complex, data-intensive environment
Practical experience applying AI coding assistants and agentic tools to engineering workflows
Visa sponsorship is available; candidates must be legally authorized to work in the United States
Master's degree in Computer Science, Data Engineering, or a related field is advantageous
Experience with streaming and change-data-capture pipelines, industrial data sources and protocols, Vertex AI retrieval infrastructure, LLMs, AI-powered developer or data tooling, agent frameworks, LLM evaluation frameworks, query engines, open table formats, and enterprise data products are advantageous

Benefits

Comp & perks

Immediate medical, dental, vision and prescription drug coverage
Flexible family care days
Paid parental leave
New parent ramp-up programs
Subsidized back-up child care
Family building benefits including adoption and surrogacy expense reimbursement
Fertility treatments
Vehicle discount program for employees and family members
Management leases
Tuition assistance
Established and active employee resource groups
Paid time off for individual and team community service
Generous schedule of paid holidays, including the week between Christmas and New Year’s Day
Paid time off
Option to purchase additional vacation time