Data Engineer

Location: Arlington, TX, United States; Hybrid

Why GM Financial?

GM Financial is the wholly owned captive finance subsidiary of General Motors and is headquartered in Fort Worth, U.S. We are a global provider of auto finance solutions, with operations in North America, South America, and the Asia Pacific region. Through our long-standing relationships with auto dealers, we offer attractive retail financing and lease programs to meet the needs of each customer. We also offer commercial lending products to dealers to help them finance and grow their businesses.

At GM Financial, our team members define and shape our culture — an environment that welcomes new ideas, fosters integrity, and creates a sense of community and belonging. Here we do more than work — we thrive.

Our Purpose:  We pioneer the innovations that move and connect people to what matters.

This position will be posted until filled. 

NOTE: We are unable to consider candidates who require visa sponsorship now or in the future for this position. 

About the role:

This role focuses on designing, integrating, and managing data pipelines that collect, transform, and monitor data from internal and external sources to support business and analytics needs. The position collaborates with business stakeholders, data engineering teams, and privacy compliance partners to implement scalable data solutions, improve data processes, and evaluate emerging technologies that drive innovation and operational efficiency.

We are expanding our efforts into complimentary data technologies for decision support in areas of ingesting and processing large data sets including data commonly referred to as semi-structured or unstructured data. Our interests are in enabling data science and search-based applications on large and low latent data sets in both a batch and streaming context for processing. To that end, this role will engage with team counterparts in exploring and deploying technologies for creating data sets using a combination of batch and streaming transformation processes. These data sets support both off-line and in-line machine learning training and model execution. Other data sets support search engine-based analytics. Exploration and deployment of technologies activities include identifying opportunities that impact business strategy, collaborating on the selection of data solutions software, and contributing to the identification of hardware requirements based on business requirements.