General Motors Jobs

Senior Data Engineer

General Motors

Senior Data Engineer

Posted 2 Hours Ago

Be an Early Applicant

Austin, TX, USA

Hybrid

125K-192K Annually

Senior level

Automotive • Big Data • Information Technology • Robotics • Software • Transportation • Manufacturing

We make amazing products people love, for every journey.

The Role

Designs, builds, and operates batch and real-time connected-vehicle data pipelines and products. Develops streaming systems, APIs, cloud-native workloads, observability, and secure data capabilities using Azure, Flink, Spark, Java, Python, and SQL. Applies machine learning and generative AI, including retrieval-augmented generation and agentic workflows, to data engineering and operational problems. Participates in architecture, testing, incident response, on-call support, mentoring, and cross-functional technical leadership.

Summary Generated by Built In

Description
This role is categorized as hybrid. This means the successful candidate is expected to report to Austin Technical Center three times per week, at minimum [or other frequency dictated by the business if more than 3 days].
The Role
Vehicle Data Engineering is looking for a Senior Data Engineer to design, build, and operate data products that transform connected-vehicle signals into trusted insights, health information, and proactive customer experiences. This role will work across real-time streaming, cloud data platforms, APIs, analytics, and artificial intelligence to deliver secure, reliable, and explainable data capabilities at scale.
The ideal candidate is a hands-on technical leader who can move from architecture to production implementation, improve engineering standards, and partner effectively with product, software, vehicle, cloud, analytics, and data-governance teams.
What You'll Do

Design and develop production-grade batch and real-time data pipelines for connected-vehicle telemetry, trip and session data, diagnostic signals, and vehicle-health indicators.
Build streaming applications that ingest, enrich, validate, deduplicate, curate, and publish event-driven data for downstream services, notifications, reporting, and analytics.
Develop reliable data products using Apache Flink, Apache Spark Structured Streaming, Java, Python, and SQL.
Work with Azure services including Azure Kubernetes Service, Event Hubs, Azure Data Explorer, Azure Key Vault, Azure Databricks, Azure Monitor, and Application Insights.
Design and maintain data contracts, schemas, APIs, and event models using GraphQL, REST, gRPC, JSON, and cloud-event patterns.
Apply artificial intelligence and machine learning to data engineering problems such as anomaly detection, data-quality triage, predictive health signals, intelligent operations, and engineering productivity.
Build or integrate generative artificial intelligence capabilities, including large language model applications, embeddings, vector search, retrieval-augmented generation, agentic workflows, prompt engineering, evaluation, and safety guardrails.
Create automated tests, performance benchmarks, integration tests, and validation checks for high-volume data and event-driven systems.
Establish observability with OpenTelemetry, Datadog, Grafana, Prometheus, dashboards, monitors, service-level objectives, and actionable alerts.
Secure data in transit and at rest and apply privacy, consent, retention, lineage, access-control, and regional compliance requirements to vehicle and location data.
Automate infrastructure and delivery using Kubernetes, Helm, Argo CD, Terraform, continuous integration, continuous delivery, and infrastructure-as-code practices.
Participate in architecture reviews, code reviews, incident response, root-cause analysis, operational readiness, and on-call support as needed.
Mentor engineers, raise technical standards, document design decisions, and contribute to a culture of quality, ownership, and continuous improvement.

Your Skills & Abilities (Required Qualifications)

Bachelor's degree in computer science, computer engineering, data engineering, information systems, or a related technical field, or equivalent experience.
5+ years of professional experience in data engineering, software engineering, distributed systems, or a related field.
Strong hands-on experience with Java or Python, SQL, object-oriented design, data structures, algorithms, and automated testing.
Experience designing and operating production data pipelines using Apache Flink, Apache Spark, Kafka, Azure Event Hubs, or comparable streaming technologies.
Experience with cloud-native development on Microsoft Azure and containerized workloads running on Kubernetes.
Experience with Databricks, Delta Lake, distributed data processing, data modeling, and performance optimization.
Experience designing APIs and event-driven systems using GraphQL, REST, gRPC, asynchronous HTTP clients, or equivalent technologies.
Experience with schema evolution, data contracts, data-quality validation, lineage, observability, and privacy-aware data handling.
Demonstrated experience applying machine learning, artificial intelligence, or generative artificial intelligence in a production engineering, analytics, or data-product environment.
Working knowledge of large language models, embeddings, vector databases or vector search, retrieval-augmented generation, prompt design, model evaluation, and responsible artificial intelligence practices.
Ability to troubleshoot complex distributed systems and communicate technical decisions clearly to both technical and nontechnical audiences.
Ability to work effectively in a collaborative, agile, cross-functional environment.

What Can Give You a Competitive Advantage (Preferred Qualifications)

Master's degree in computer science, data science, artificial intelligence, machine learning, or a related field.
Experience building multi-agent or agentic systems for data analysis, data operations, engineering support, or customer-facing insights.
Experience with Databricks artificial intelligence and machine learning capabilities, MLflow, model registries, feature stores, vector search, or model-serving platforms.
Experience designing retrieval-augmented generation systems, including chunking, embedding strategies, hybrid retrieval, reranking, grounding, citation, offline evaluation, online evaluation, and hallucination mitigation.
Experience applying generative artificial intelligence to data observability, incident summarization, root-cause analysis, schema mapping, documentation generation, or pipeline remediation.
Experience with time-series data, vehicle telemetry, geospatial data, diagnostics, predictive maintenance, anomaly detection, or other high-volume sensor data.
Experience with Azure OpenAI Service or comparable large language model platforms and with securing enterprise AI workloads.
Experience with Python data and machine-learning libraries such as pandas, NumPy, scikit-learn, PyTorch, or equivalent tools.
Experience with Terraform, Helm, Argo CD, GitHub Actions, Azure DevOps, or comparable DevOps platforms.
Experience with OpenTelemetry, Datadog, Grafana, Prometheus, distributed tracing, service-level objectives, and production reliability engineering.
Knowledge of data catalogs, governance platforms, consent management, privacy engineering, and regional data-retention requirements.
Strong technical leadership, mentoring, influencing, documentation, and cross-functional communication skills.
Demonstrated initiative, sound judgment, accountability, curiosity, and ability to simplify complex problems.

This job may be eligible for relocation benefits.
Compensation:

The expected base compensation for this role is: $125,000 - $191,500. Actual base compensation within the identified range will vary based on factors relevant to the position.
Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.
Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more.

GM DOES NOT PROVIDE IMMIGRATION-RELATED SPONSORSHIP FOR THIS ROLE. DO NOT APPLY FOR THIS ROLE IF YOU WILL NEED GM IMMIGRATION SPONSORSHIP NOW OR IN THE FUTURE. THIS INCLUDES DIRECT COMPANY SPONSORSHIP, ENTRY OF GM AS THE IMMIGRATION EMPLOYER OF RECORD ON A GOVERNMENT FORM, AND ANY WORK AUTHORIZATION REQUIRING A WRITTEN SUBMISSION OR OTHER IMMIGRATION SUPPORT FROM THE COMPANY (e.g., H-1B, OPT, STEM OPT, CPT, TN, J-1, etc.)
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About GM
Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.
Why Join Us
We believe we all must make a choice every day - individually and collectively - to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team.
Total Rewards | Benefits Overview
From day one, we're looking out for your well-being-at work and at home-so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources.
Non-Discrimination and Equal Employment Opportunities (U.S.)
General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers.
All employment decisions are made on a non-discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws.
We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire.
Accommodations
General Motors offers opportunities to all job seekers including individuals with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, email us [email protected] or call us at 1-800-865-7580. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.

Skills Required

Bachelor's degree in computer science, computer engineering, data engineering, information systems, or a related technical field, or equivalent experience
5+ years of professional experience in data engineering, software engineering, distributed systems, or a related field
Strong hands-on experience with Java or Python, SQL, object-oriented design, data structures, algorithms, and automated testing
Experience designing and operating production data pipelines using Apache Flink, Apache Spark, Kafka, Azure Event Hubs, or comparable streaming technologies
Experience with Microsoft Azure cloud-native development and containerized workloads running on Kubernetes
Experience with Databricks, Delta Lake, distributed data processing, data modeling, and performance optimization
Experience designing APIs and event-driven systems using GraphQL, REST, gRPC, asynchronous HTTP clients, or equivalent technologies
Experience with schema evolution, data contracts, data-quality validation, lineage, observability, and privacy-aware data handling
Demonstrated experience applying machine learning, artificial intelligence, or generative artificial intelligence in a production engineering, analytics, or data-product environment
Working knowledge of large language models, embeddings, vector databases or vector search, retrieval-augmented generation, prompt design, model evaluation, and responsible artificial intelligence practices
Ability to troubleshoot complex distributed systems and communicate technical decisions clearly to technical and nontechnical audiences
Ability to work effectively in a collaborative, agile, cross-functional environment
Master's degree in computer science, data science, artificial intelligence, machine learning, or a related field
Experience building multi-agent or agentic systems for data analysis, data operations, engineering support, or customer-facing insights
Experience with Databricks AI and machine learning capabilities, MLflow, model registries, feature stores, vector search, or model-serving platforms
Experience designing retrieval-augmented generation systems, including chunking, embedding strategies, hybrid retrieval, reranking, grounding, citation, evaluation, and hallucination mitigation
Experience applying generative artificial intelligence to data observability, incident summarization, root-cause analysis, schema mapping, documentation generation, or pipeline remediation
Experience with time-series data, vehicle telemetry, geospatial data, diagnostics, predictive maintenance, anomaly detection, or high-volume sensor data
Experience with Azure OpenAI Service or comparable large language model platforms and securing enterprise AI workloads
Experience with Python data and machine-learning libraries such as pandas, NumPy, scikit-learn, PyTorch, or equivalent tools
Experience with Terraform, Helm, Argo CD, GitHub Actions, Azure DevOps, or comparable DevOps platforms
Experience with OpenTelemetry, Datadog, Grafana, Prometheus, distributed tracing, service-level objectives, and production reliability engineering
Knowledge of data catalogs, governance platforms, consent management, privacy engineering, and regional data-retention requirements
Strong technical leadership, mentoring, influencing, documentation, and cross-functional communication skills
Demonstrated initiative, sound judgment, accountability, curiosity, and ability to simplify complex problems

What the Team is Saying

General Motors Compensation & Benefits Highlights

Retirement Support — For many U.S. salaried roles, GM contributes 4% automatically and matches up to 6% on deferrals, enabling up to 10% into the 401(k); hourly/represented plans also include documented company contributions. This structure is prominently detailed in GM’s careers and corporate materials.
Leave & Time Off Breadth — GM advertises 15+ paid vacation days and up to 19 paid holidays for eligible employees, alongside flexible work arrangements. These time‑off provisions are consistently highlighted across GM’s benefits materials.
Parental & Family Support — GM states 12 weeks of paid parental leave after one year of service and highlights family‑building support with a $40,000 combined lifetime maximum for fertility, surrogacy, and adoption. These benefits are described in GM’s careers pages and supporting sources.

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The Company

HQ: Detroit, MI

165,000 Employees

Year Founded: 1908

What We Do

At General Motors, our vision is to create a world with Zero Crashes, Zero Emissions, and Zero Congestion. We wholeheartedly embrace the responsibility to lead the change that will make our world better, safer, and more equitable for all. Our industry and company are undergoing a once-in-a-lifetime technological transformation, which is reshaping our approach to technology and innovation. We are expanding our horizons through new technology platforms and driving innovations that deliver exceptional value to our customers.

Why Work With Us

At General Motors, our purpose is to pioneer the innovations that move and connect people to what matters. We’re driving the world forward, together. We’re building vehicle software alongside its hardware, hands-free driving that will lead to autonomy, and EVs that charge your home for an all-electric future.

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Hybrid Workspace

Employees engage in a combination of remote and on-site work.

Roles that are categorized as Hybrid mean that the successful candidate is expected to report onsite to the designated facility at least three times per week or other frequency as dictated by the business.

Typical time on-site: 3 days a week