Senior Feature Engineer

General Motors

2d•Remote

About The Position

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.

Requirements

5+ years of experience in feature engineering, data engineering, or ML engineering roles supporting AI/ML model development
Strong hands-on experience with Databricks (Spark clusters, notebooks, Delta Lake, workflows/jobs)
Proficiency in Python for data pipeline and feature transformation development (pandas, PySpark, 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 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

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).

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