Senior Data Engineer

GM•Warren, MI

13d•Hybrid

About The Position

This role is for a senior individual contributor in Data Engineering who can independently lead complex technical work, apply strong professional judgment, and improve processes, services, and delivery patterns across the organization. At this level, the individual is expected to operate with minimal guidance, resolve non-standard problems using advanced analytical thinking, and serve as a technical resource for less experienced team members. The role is anchored in data engineering and includes an additional data science profile to strengthen AI data enablement, experimentation support, and close collaboration with data scientists and business partners. Data engineers at GM are expected to build and maintain reliable, scalable data infrastructure, transform raw data into high-quality datasets for analytics and advanced data science use cases, and partner closely with data scientists, analysts, software engineers, and business teams. Data scientists are expected to apply analytical and machine learning techniques, explore and prepare data, validate models, design experiments, and translate findings into actionable recommendations.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, or related field, or equivalent experience.
5+ years of relevant full-time experience; or equivalent knowledge
Strong experience in data engineering, including pipeline development, data modeling, data integration, and production support for enterprise data platforms.
Data engineering experience, including Python or Scala, SQL, and relational/non-relational data storage (ETL frameworks, big data processing, NoSQL).
Experience with cloud platforms – Azure preferred; AWS or GCP also considered.
Proficiency with SQL, key-value datastores, and document stores
Design, build, and optimize scalable batch and streaming data pipelines using Databricks (Apache Spark, Delta Lake) to support Medallion Architecture.
Experience with modern cloud data platforms, distributed processing, and production-grade data pipelines.
Demonstrated ability to work independently, manage broad technical challenges, and deliver solutions with limited guidance.
Experience collaborating across engineering, analytics, and business teams to deliver data solutions that support measurable outcomes.
Understanding of statistics, machine learning, experimentation, and data mining concepts used to drive informed decisions.
Ability to prepare and explore data, support model development workflows, and help validate analytical outputs in partnership with data scientists.
Ability to translate analytical needs into scalable data solutions and help move data science work from exploration into repeatable, governed delivery.
Strong communication skills to connect technical work with business value and convert complex findings into clear recommendations.

Nice To Haves

Master’s degree in Computer Science, Software Engineering, or related field
Experience supporting AI or machine learning use cases through strong data foundations, feature engineering support, experimentation enablement, or model-ready data preparation.
Experience working in environments where data engineering and data science operate closely together to deliver business value at scale.
Demonstrated mentoring, technical leadership, and process improvement impact consistent with a Level 7 senior individual contributor role.

Responsibilities

Design, build, and productionize reliable, scalable, and secure data pipelines and data products in Azure Databricks that support AI, analytics, and operational use cases.
Lead the transformation of raw data from multiple source systems into trusted, well-structured datasets suitable for downstream analytics, model development, and AI enablement.
Drive improvements in internal processes, delivery patterns, and technical solutions that support broader functional strategy and increase team efficiency.
Solve complex and non-standard data engineering problems using advanced analytical and problem-solving techniques, with strong ownership and sound technical judgment.
Partner closely with data scientists, analysts, software engineers, product stakeholders, and business teams to ensure data is accessible, trustworthy, and aligned to business outcomes.
Support AI and data science use cases by enabling high-quality feature-ready data, experimentation workflows, and scalable patterns for model development and deployment.
Contribute to technical direction within the team, influence key engineering decisions in their area, and act as a go-to resource for peers and less experienced engineers.
Mentor team members through technical guidance, knowledge sharing, and strong engineering practices consistent with Level 7 expectations.