Staff Data Engineer

General Motors (GM)

Confirmed live yesterday High trust

Closes in 7 days Hybrid

Quick summary

Work type: Hybrid
Location: Warren, MIAustin, TX
Salary: $160,200–$211,950 / yr
Posted: 7 days ago
Freshness: Confirmed live yesterday
Closes: Sep 18, 2026 (soon)
Nearby: 99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $190k

This role $186k

$143k most similar roles pay here $237k

This role pays less than 57% of similar roles. Most pay $159,875–$219,425 — the shaded band above. At the midpoint, this role pays about $186k versus about $190k for comparable roles.

Based on 240 similar postings.

Employer

About General Motors (GM)

General Motors (GM) is a leading American multinational automotive corporation founded in 1908 and headquartered in Detroit, Michigan.

General Motors (GM) currently has 116 open roles on FindRole.

Listed pay typically runs $160,200–$245,000 across 59 roles with salary data.

Most-posted roles

View all roles at General Motors (GM)

At a glance

TL;DR · Staff Data Engineer

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Staff Data Engineer The Staff Data Engineer joins a technical team to lead complex, cross-team initiatives and establish direction for key data domains while improving organizational processes and delivery patterns. This role focuses on building and maintaining reliable, scalable data infrastructure by transforming raw data from heterogeneous sources into high-quality datasets for analytics and AI enablement. The engineer will design and productionize secure pipelines in Azure Databricks using Apache Spark and Delta Lake to support Medallion Architecture standards. Key responsibilities include developing feature-ready data products, establishing MLOps practices, and mentoring other engineers. Required technical skills include proficiency in Python or Scala, SQL performance tuning, and experience with relational and non-relational storage. The role addresses the challenge of moving from fragmented data to governed, contract-backed data products that power advanced machine learning models and automated intelligence systems.

What does a Data Engineer earn?

Median $183138 from 214 postings across 58 companies.

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Skills

Azure Databricks Apache Spark Delta Lake Python Scala SQL NoSQL Kafka Event Hubs CI/CD MLOps DataOps Medallion Architecture Data Modeling Distributed Processing Data Governance Data Quality Observability

What you'll do

Design and productionize scalable data pipelines and products in Azure Databricks for AI and analytics.
Transform raw data from heterogeneous sources into high-quality, governed datasets for downstream use cases.
Define and champion architecture standards, including Medallion Architecture and Delta Lake protocols, across the organization.
Lead AI enablement by delivering feature-ready data and scalable workflows for model development and monitoring.
Solve complex, non-standard data engineering problems using advanced analytical thinking and technical judgment.
Mentor engineers and data scientists through code reviews, technical guidance, and knowledge sharing.
Establish infrastructure for MLOps, including CI/CD pipelines, testing strategies, and data observability.
Communicate complex technical trade-offs and roadmaps to both technical and non-technical stakeholders.

What we're looking for

Bachelor's degree in Computer Science, Software Engineering, Data Engineering, or a related field.
8+ years of full-time experience in data engineering or closely related roles.
Proficiency in Python or Scala and SQL for large-scale dataset performance tuning.
Experience building production-grade pipelines using Databricks, Apache Spark, and Delta Lake.
Expertise in cloud platforms, with a strong preference for Azure.
Experience supporting AI/ML use cases through high-quality data preparation and experimentation workflows.
Knowledge of relational and non-relational data storage technologies including NoSQL and key-value stores.
Ability to lead cross-functional collaboration and communicate complex technical concepts to diverse audiences.

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