Staff Data Engineer – Telematics Data Products

General Motors•Austin, TX

1d•$160,200 - $211,950•Hybrid

About The Position

This role is categorized as hybrid. This means the successful candidate is expected to report to Warren Global Technical Center or Austin Technical Center three times per week, at minimum [or other frequency dictated by the business if more than 3 days]. We're looking for a hands-on Staff Data Engineer to build the next generation of telematics data products that power analytics, AI, and decision-making across the enterprise. This role sits at the intersection of data architecture, analytics, and platform engineering. Success isn't measured by pipelines alone—it's about creating trusted, reusable, analytics-ready data products that make complex telemetry data easier to discover, understand, and use at scale. You'll help define the foundational datasets, shared metrics, and curated data products that enable teams across engineering, product, quality, safety, and operations to unlock value from connected vehicle data. This is an opportunity to help define how a large-scale telematics platform supports analytics, AI, and enterprise decision-making. The data products you build will help teams answer questions such as: How many miles were driven with Super Cruise last week? Which vehicle signals are most correlated with battery-related warranty events? How are connected services performing across vehicle programs? What emerging trends are appearing across EV systems, charging, and battery health? Which ADAS and safety systems are generating the highest diagnostic or service activity? Where should the next wave of EV charging infrastructure be deployed? How can telemetry data improve product quality, warranty performance, and future vehicle development? You'll help move the organization from custom analytics and duplicated logic toward trusted, reusable data products that scale across the enterprise.

Requirements

Bachelor's degree in Computer Science, Data Engineering, Software Engineering, Information Systems, Mathematics, Statistics, or a related technical field
8+ years of experience in data engineering or analytics platforms, including experience operating at a senior or staff level with responsibility for architecture, technical direction, and cross-team impact
Deep hands-on experience with Databricks
Strong data modeling, semantic design, and data architecture skills
Expertise in SQL, Spark, and distributed data processing
Experience designing solutions for performance, scalability, reliability, and cost optimization
Strong understanding of how analysts, engineers, data scientists, and ML practitioners consume data
Experience working with IoT, telemetry, event-stream, or sensor data
Experience enabling AI, machine learning, and advanced analytics through governed and reusable data products
Builder mindset with strong technical judgment and a bias toward simplification

Nice To Haves

Master's degree in Computer Science, Data Engineering, Data Science, Analytics, or a related discipline
Ability to think deeply about: How data should be modeled
How analytics should work
How reusable data products should be designed
How platform capabilities can reduce duplication and wasted effort
How usage patterns should drive future investments

Responsibilities

Design and build reusable telematics data products that make analytics faster, simpler, and more scalable across the enterprise
Design canonical data products that make telematics data easier to understand, join, and reuse
Build curated analytics-ready datasets that standardize metrics, aggregations, and business logic across teams
Turn repeated analytical work into scalable, reusable data products
Use data usage patterns and adoption signals to help prioritize the data product roadmap
Design and implement scalable solutions in Azure Databricks
Improve performance, scalability, reliability, and cost efficiency
Enable self-service analytics by making data easier to discover, trust, and consume
Build data products that support analytics, machine learning, and AI use cases