Senior Feature Engineer

GM•Warren, MI

1d•Hybrid

About The Position

General Motors is seeking a Senior Feature Engineer to join its Data & Feature Engineering team. The team builds the shared feature store, governance model, and platform tooling that support advanced analytics and machine learning solutions aimed at improving product reliability and performance. The Senior Feature Engineer will lead the design, development, and operation of a governed feature platform, including shared infrastructure, standards, and tooling that domain teams use to define, certify, and serve reusable features for product analytics. The role bridges data engineering and applied machine learning, translating raw, high-volume data into reliable, reusable, production-grade features for data scientists and ML engineers. The position is categorized as hybrid, with the selected candidate expected to report to a specific location at least three times a week or at another frequency set by their manager. The role is not eligible for relocation benefits. General Motors does not provide immigration-related sponsorship for this role.

Requirements

At least 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 an equivalent degree.
Strong hands-on experience with Databricks, including Spark clusters, notebooks, Delta Lake, and workflows/jobs.
Proficiency in Python for data pipeline and feature transformation development, including pandas, PySpark, and modular pipeline design.
Strong SQL skills for complex data transformation, aggregation, and optimization.
Solid working knowledge of Apache Spark (PySpark or Spark SQL) for distributed data processing at scale.
Experience building and maintaining CI/CD pipelines for data/ML pipeline code.
Demonstrated experience implementing data quality management practices, including 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.

Nice To Haves

At least 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 such as Kafka or Delta LIVE Tables.

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, including notebooks, jobs, Delta Lake, and 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, including automated testing, deployment, and rollback.
Define and enforce data quality checks, including completeness, freshness, schema validation, drift detection, and anomaly detection.
Optimize Spark and 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.