Senior Feature Engineer

Description

The company 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.

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+ years of 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 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

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).

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., H1-B, OPT, STEM OPT, CPT, TN, J-1, etc).

This role is categorized as hybrid. This means the selected candidate is expected to report to a specific location at least 3 times a week {or other frequency dictated by their manager}.

This job is not eligible for relocation benefits. Any relocation costs would be the responsibility of the selected candidate.

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.

Responsibilities

• The company 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.
• 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+ years of 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 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
• 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).
• 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., H1-B, OPT, STEM OPT, CPT, TN, J-1, etc).
• This role is categorized as hybrid. This means the selected candidate is expected to report to a specific location at least 3 times a week {or other frequency dictated by their manager}.
• This job is not eligible for relocation benefits. Any relocation costs would be the responsibility of the selected candidate.
• 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.

Benefits

• 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 the company team.
• 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.)
• The company is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers.
• We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire .