Senior Feature Engineer

General Motors Company; Austin (TX); On-site; USD 120,000 - 190,000; Full time

Job summary

General Motors is seeking a Senior Feature Engineer to lead design, development, and operation of a governed feature platform and shared infrastructure for product analytics. You will translate data into reliable, production-grade features for data scientists and ML engineers, collaborating across data science, ML, and platform teams.

This role emphasizes building scalable pipelines, feature stores, and CI/CD, with Databricks and Spark expertise to enable train/serve parity and strong data

Qualifications

• 5+ years of experience in feature engineering, data engineering, or ML engineering roles supporting AI/ML model development.
• Bachelor's degree in Computer Science, Mechanical Engineering, Electrical Engineering, Data Analytics or equivalent degree
• Strong hands-on experience with Databricks(Spark clusters, notebooks, Delta Lake, workflows/jobs)
• Proficiency inPython for data pipeline and feature transformation development (pandas, PySpark, modular pipeline design)
• StrongSQLskills for complex data transformation, aggregation, and optimization
• Solid working knowledge ofApache Spark(PySpark or Spark SQL) for distributed data processing at scale
• Experience building and maintainingCI/CD pipelinesfor data/ML pipeline code
• Demonstrated experience implementingdata quality managementpractices validation frameworks, monitoring, and alerting
• Understanding of ML workflows and how feature quality impacts model performance
• Experience with version control (Git) and collaborative software development practices
• Strong communication skills and ability to work cross-functionally with data science, ML engineering, and platform teams

Responsibilities

• Design, develop, and maintain scalable feature engineering pipelines for ML/AI model development and production inference
• Build and manage feature transformation logic using Python, SQL, and Apache Spark across large, distributed datasets
• Develop and operate feature pipelines within Databricks (notebooks, jobs, Delta Lake, Unity Catalog)
• Collaborate with data scientists and ML engineers to translate modeling requirements into well-defined, reusable features
• Establish and maintain a feature store (or equivalent) to enable feature reuse, versioning, and consistency between training and inference (train/serve parity)
• Implement CI/CD pipelines for feature and data pipeline code — automated testing, deployment, and rollback for pipeline changes
• Define and enforce data quality checks (completeness, freshness, schema validation, drift detection, anomaly detection) across feature pipelines
• Optimize Spark/Databricks jobs for performance, cost, and reliability at scale
• Document feature definitions, lineage, and pipeline architecture for reproducibility and audit purposes
• Partner with data engineering and platform teams to ensure features integrate cleanly with upstream data sources and downstream ML platforms
• Monitor production feature pipelines and proactively resolve data quality or pipeline failures

Skills

Feature engineering

Data engineering

ML engineering

Python

SQL

Apache Spark

Git

CI/CD

Data quality

Cross-functional collaboration

ML workflows

Education

Bachelor's degree

Tools

Databricks

Delta Lake

Unity Catalog

Apache Spark

Job description

The Role:

General Motors is seeking a highly motivated and qualified candidate for the position of Senior Feature Engineer on our Data & Feature Engineering team. This team builds the shared feature store, governance model, and platform tooling that power advanced analytics and machine learning solutions aimed at improving product reliability and performance.

As a Senior Feature Engineer, you will lead the design, development, and operation of our governed feature platform, the shared infrastructure, standards, and tooling that domain teams use to define, certify, and serve reusable features for product analytics. This role sits at the intersection of data engineering and applied ML translating raw, high-volume data into reliable, reusable, production-grade features that data scientists and ML engineers can trust.

Key Responsibilities

• Design, develop, and maintain scalable feature engineering pipelines for ML/AI model development and production inference
• Build and manage feature transformation logic using Python, SQL, and Apache Spark across large, distributed datasets
• Develop and operate feature pipelines within Databricks (notebooks, jobs, Delta Lake, Unity Catalog)
• Collaborate with data scientists and ML engineers to translate modeling requirements into well-defined, reusable features
• Establish and maintain a feature store (or equivalent) to enable feature reuse, versioning, and consistency between training and inference (train/serve parity)
• Implement CI/CD pipelines for feature and data pipeline code — automated testing, deployment, and rollback for pipeline changes
• Define and enforce data quality checks (completeness, freshness, schema validation, drift detection, anomaly detection) across feature pipelines
• Optimize Spark/Databricks jobs for performance, cost, and reliability at scale
• Document feature definitions, lineage, and pipeline architecture for reproducibility and audit purposes
• Partner with data engineering and platform teams to ensure features integrate cleanly with upstream data sources and downstream ML platforms
• Monitor production feature pipelines and proactively resolve data quality or pipeline failures

Required Qualifications

• 5+ yearsof experience in feature engineering, data engineering, or ML engineering roles supporting AI/ML model development
• Bachelors degree Computer Science, Mechanical Engineering, Electrical Engineering, Data Analytics or equivalent degree
• Strong hands-on experience withDatabricks(Spark clusters, notebooks, Delta Lake, workflows/jobs)
• Proficiency inPythonfor data pipeline and feature transformation development (pandas, PySpark, modular pipeline design)
• StrongSQLskills for complex data transformation, aggregation, and optimization
• Solid working knowledge ofApache Spark(PySpark or Spark SQL) for distributed data processing at scale
• Experience building and maintainingCI/CD pipelinesfor data/ML pipeline code
• Demonstrated experience implementingdata quality managementpractices validation frameworks, monitoring, and alerting
• Understanding of ML workflows and how feature quality impacts model performance
• Experience with version control (Git) and collaborative software development practices
• Strong communication skills and ability to work cross-functionally with data science, ML engineering, and platform teams

What Will Give You A Competitive Edge (Preferred Skills)

• 8+ years of experience in data engineering.
• Experience with vehicle telematics, battery health data, or embedded systems.
• Familiarity with machine learning workflows and model deployment.
• Experience with real-time data streaming technologies (e.g., Kafka, Delta LIVE Tables).