Senior Data Engineer

Job 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 We are looking for a hands-on Machine Learning Engineer with at least seven years of experience to productionize machine learning for manufacturing and to build the data engineering that those models depend on.

This person will design, train, validate, and deploy models, and will design and operate the ETL/ELT pipelines, feature data, and governed Bronze, Silver, and Gold products those models run on. Current, working knowledge of Databricks is required. What You’ll Do Productionize manufacturing machine learning, including training, validation, batch scoring, deployment, monitoring, and retraining workflows in Databricks.

Design and build the data engineering underneath those models: ingestion, transformation, curation, feature pipelines, and governed tables and volumes in Unity Catalog. Build reusable Bronze, Silver, and Gold pipelines and medallion patterns that analytics and ML both consume. Use MLflow and Unity Catalog for experiment tracking, model registration, versioning, lineage, and controlled promotion across environments.

Ingest from enterprise and plant sources with managed connectors, DLT, Auto Loader, and structured streaming where those fit. Partner with data science, architecture, product, manufacturing, and platform teams using Python, Spark, SQL, and Databricks-native tools. Build CI/CD for model and data products, including tests, validation gates, deployment, code review, and operational handoff.

Improve reliability through data validation, evaluation sets, monitoring, alerting, documentation, and runbooks. Support migration from legacy and on-prem ETL, and from proof-of-concept models, into production Databricks patterns. Participate in operational support and on-call rotation for production data and ML pipelines.

Your Skills & Abilities (Required Qualifications) 6+ of experience in machine learning engineering, data engineering, or both. Bachelor’s degree in Computer Science, Software Engineering, Data Science, or a related field, or equivalent experience. Proven experience deploying machine learning models into production and building the data or feature pipelines that support them.

Strong proficiency in Scala, Python, SQL, Spark, and Databricks, with production-quality software engineering practices. Strong experience building and supporting cloud-based ETL/ELT pipelines, data platforms, and pipeline orchestration patterns for both batch and streaming operations. Experience with MLflow or an equivalent model lifecycle: experiment tracking, registry, versioning, and promotion across environments.

Strong experience with Databricks jobs, workflows, repos, Unity Catalog, and MLflow-based experimentation and model lifecycle management. CI/CD and GitHub-based deployment of data and ML products. Experience delivering production pipelines with data science, architecture, product, and business partners. Strong troubleshooting, communication, and technical leadership.

What Can Give You a Competitive Advantage (Preferred Qualifications) Data science experience is a plus, including building and training a model, feature design, model evaluation, and working with data scientists to turn experiments into production candidates. Experience deploying a model into an isolated network, such as a plant or other restricted environment, where the runtime is separated from the training platform.

Scala, Azure Event Hub, Kafka, or Pulsar. DLT, Auto Loader, structured streaming, and metadata-driven pipeline frameworks. Model monitoring, drift detection, validation gates, and production-readiness reviews. Manufacturing data, plant-floor systems, industrial telemetry, or manufacturing quality use cases. Legacy ETL modernization or the move from proof of concept into a governed production pattern.

Globally distributed teams and concurrent initiatives. This job may be eligible for relocation benefits.

Compensation

The expected base compensation for this role is: $133,000 - $188,600. 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.) #LI-CC1 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.

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.

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