Descripción

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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Información sobre diversidad

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Adecuaciones (EE.UU. y Canadá)

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