Senior Data Engineer

GM•Austin, TX

1d•Hybrid

About The Position

This role is categorized as hybrid. This means the successful candidate is expected to report to Austin Technical Center three times per week, at minimum [or other frequency dictated by the business if more than 3 days]. We are looking for a hands-on Senior Data Engineer to help build the data products that power Customer Care and Aftersales analytics, AI, and operational decision-making. Behind every warranty claim, repair order, service event, and customer interaction is an opportunity to improve quality, reduce costs, increase customer satisfaction, and make vehicle ownership better. The challenge is not collecting the data. It is turning large, complex operational datasets into trusted, reusable assets that teams can use to answer important business questions quickly and confidently. If you enjoy building scalable data solutions that make answers possible to questions like the ones below, this role is for you. Which repair orders are keeping customer vehicles on dealer lots the longest? What parts shortages are creating the greatest service delays? Which warranty concerns are increasing across vehicle programs? Where are repeat repairs occurring most frequently? What trends are emerging across customer care and service operations?

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or a related technical field
5+ years of experience in data engineering, software engineering, or related disciplines
Expert-level SQL skills with extensive experience building, optimizing, and troubleshooting complex analytical workloads
Strong software engineering skills in Python or Java
Experience building production-grade data pipelines and distributed data processing solutions
Experience with Databricks or similar large-scale data platforms
Experience working in cloud environments such as Azure, AWS, or GCP
Experience with Kafka, Pulsar, Event Hub, or similar streaming technologies
Experience optimizing large-scale data workloads for performance, reliability, and cost efficiency
Experience with CI/CD, observability, monitoring, and automation practices
Strong understanding of data quality, governance, and operational excellence
Strong communication skills and the ability to work closely with business and technical stakeholders

Nice To Haves

The ideal candidate is a builder. They care about more than moving data from one place to another. They care about creating trusted, scalable data products that people actually use. They are equally comfortable writing complex SQL, building production-grade data pipelines, optimizing distributed workloads, and helping business users turn ideas into governed, reusable capabilities. Most importantly, they know how to balance speed, scalability, reliability, and cost as data products grow, and they help the broader team do the same.

Responsibilities

Build and support data products that power analytics, AI, reporting, and operational decision-making
Design and develop scalable pipelines that ingest, transform, curate, and publish high-volume operational data
Create trusted datasets that make aftersales data easier to understand, use, and reuse
Optimize data processing workloads for performance, scalability, reliability, and cost efficiency
Implement data quality, governance, observability, and monitoring capabilities
Build reusable frameworks that enable analysts and domain experts to operationalize SQL and Python solutions in a governed, scalable manner
Partner with business stakeholders, analysts, architects, and engineers to transform business requirements into trusted data products
Mentor engineers and contribute to engineering standards and best practices