Senior Data Engineer

GM • Austin, TX

1d • Hybrid

About The Position

This hybrid role is based at the Austin Technical Center, with the successful candidate expected to report onsite at least three times per week (or more frequently if business needs require). The role focuses on productionizing machine learning for manufacturing and building the data engineering infrastructure those models depend on. The engineer will design, train, validate, and deploy models, and design and operate ETL/ELT pipelines, feature data, and governed Bronze, Silver, and Gold data products in Databricks. Current working knowledge of Databricks is required.

The role includes productionizing manufacturing ML workflows; building ingestion, transformation, curation, and feature pipelines; managing governed tables and volumes in Unity Catalog; and using MLflow and Unity Catalog for model lifecycle management. The engineer will collaborate with data science, architecture, product, manufacturing, and platform teams, build CI/CD for data and ML products, improve reliability, modernize legacy ETL and proof-of-concept models, and participate in production support and an on-call rotation.

Requirements

• At least 6 years 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 supporting data or feature pipelines.
• 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 orchestration patterns for batch and streaming operations.
• Experience with MLflow or an equivalent model lifecycle, including 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.
• Experience with 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.

Nice To Haves

• Data science experience, including building and training models, feature design, model evaluation, and collaborating 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 separated from the training platform.
• Experience with Scala, Azure Event Hub, Kafka, or Pulsar.
• Experience with DLT, Auto Loader, structured streaming, and metadata-driven pipeline frameworks.
• Experience with model monitoring, drift detection, validation gates, and production-readiness reviews.
• Experience with manufacturing data, plant-floor systems, industrial telemetry, or manufacturing quality use cases.
• Experience modernizing legacy ETL or moving proof-of-concept work into governed production patterns.
• Experience working with globally distributed teams and concurrent initiatives.

Responsibilities

• Productionize manufacturing machine learning, including training, validation, batch scoring, deployment, monitoring, and retraining workflows in Databricks.
• Design and build model-supporting data engineering, including ingestion, transformation, curation, feature pipelines, and governed tables and volumes in Unity Catalog.
• Build reusable Bronze, Silver, and Gold pipelines and medallion patterns for analytics and ML.
• Use MLflow and Unity Catalog for experiment tracking, model registration, versioning, lineage, and controlled promotion across environments.
• Ingest enterprise and plant data using managed connectors, DLT, Auto Loader, and structured streaming where appropriate.
• 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-premises ETL, and from proof-of-concept models, into production Databricks patterns.
• Participate in operational support and an on-call rotation for production data and ML pipelines.

Benefits

• May be eligible for relocation benefits.
• Incentive pay program with payouts based on company performance, job level, and individual performance.
• Medical, dental, and vision benefits.
• Health Savings Account and Flexible Spending Accounts.
• Retirement savings plan.
• Sickness and accident benefits.
• Life insurance.
• Paid vacation and holidays.
• Tuition assistance programs.
• Employee assistance program.
• GM vehicle discounts.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior