Connected Vehicle Data Engineer

Ford Motor Company

Connected Vehicle Data Engineer at Ford applying Machine Learning models to powertrain data. Collaborating with stakeholders and resolving quality issues using big data analytics and engineering skills.

Posted 7/22/2026full-timeDearborn • Missouri • 🇺🇸 United StatesMid-LevelSenior💰 $65,100 - $166,200 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Machine Learning model development and deployment, particularly in the automotive sector, with a strong focus on data analysis, risk assessment, and stakeholder collaboration. Proficient in querying and manipulating big data to derive actionable insights and validate model predictions.

Highest-signal resume keywords

Machine Learning Model DevelopmentBigQuery SQL ProficiencyPython ProgrammingData Pipeline DesignAutomotive Industry Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningStatistical ModelingInferential AnalyticsData AnalysisSQLPythonPySparkData EngineeringAnomaly DetectionRegression

Soft Skills

Stakeholder AlignmentCommunicationProblem-SolvingCollaborationDebugging

Tools & Technologies

BigQueryATI Calibration ToolsETAS Calibration ToolsConnected Vehicle DataEnterprise Toolsets

Industry Keywords

Powertrain DataProduct DevelopmentCalibrationQualityConnected Vehicle

Tech Stack

Tools & technologies

BigQueryPySparkPythonSparkSQL

About the role

Key responsibilities & impact

Apply Machine Learning to Powertrain Data: Develop, train, and deploy machine learning models on curated powertrain data to detect anomalies, identify early-warning quality indicators, and predict component degradation.
Quantify & Assess Risk: Use statistical modeling and ML inference to quantify, assess, and prioritize risks associated with powertrain field quality issues, enabling data-driven decision-making.
Perform Inferential Analytics: Conduct inferential and diagnostic analytics to identify root causes of complex engineering and quality problems, translating CV big data into actionable insights.
Establish Stakeholder Alignment: Build strong working relationships with key stakeholders in Product Development to ensure that plans and requirements are fully understood, and issues are resolved effectively and efficiently.
Debug & Resolve Issues: Debug, root-cause, and resolve propulsion systems quality issues with cross-functional teams, leveraging connected vehicle data, ML models, and enterprise toolsets.
Foster Data Collection: Drive and optimize connected vehicle data collection strategies for solving engineering problems and characterizing customer usage patterns.
Query & Manipulate Big Data: Write highly proficient BigQuery SQL (and similar language) queries to extract, clean, and interpret massive, connected vehicle datasets in the propulsion systems domain.
Develop Data Pipelines: Design, build, and own robust data pipelines and workflows using Python, PySpark, and modern data engineering tools to support ML model training and deployment.
Coordinate Data Creation: Partner with vehicle software teams to define and create new connected vehicle data elements, and support the validation of these new telemetry signals.
Validate via Calibration Tools: Utilize in-vehicle calibration tools (ATI / ETAS) to collect high-frequency data to validate connected data and verify ML model predictions on key propulsion features and subsystems.
Synthesize & Communicate Insights: Summarize and present complex machine learning models, statistical analyses, and big data findings in a simplified, visual fashion to both technical and non-technical audiences, including executive leadership.

Requirements

What you’ll need

Bachelor’s Degree in Engineering, Data Science, Computer Science, Statistics, or a related quantitative field.
3+ years of analytical, querying, and programming experience (SQL, Python, PySpark/Spark, and similar big data tools).
2+ years of experience in the automotive industry (Product Development, Calibration, and/or Quality).
Proven experience building and applying Machine Learning models (such as supervised/unsupervised learning, regression, classification, or anomaly detection) to solve physical systems or engineering problems.

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 and more
Family building benefits including adoption and surrogacy expense reimbursement, fertility treatments, and more
Vehicle discount program for employees and family members and management leases
Tuition assistance
Established and active employee resource groups
Paid time off for individual and team community service
A generous schedule of paid holidays, including the week between Christmas and New Year’s Day
Paid time off and the option to purchase additional vacation time.