Data Engineer

Job-:
Technology

Location:

Arlington, TX, United States Workplace:
Hybrid

Schedule:

Full time Posted:
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.

Qualifications What makes You an ideal candidate?

You're a strong fit if you enjoy solving complex data challenges, working with modern cloud technologies, and building scalable solutions that turn data into meaningful insights. You're comfortable collaborating across teams, learning new technologies, and using AI- enabled tools to work more efficiently. Knowledge and Skills Experience designing, processing, and managing large-scale data using technologies such as Hadoop, Spark, Kafka, HDFS, or similar distributed platforms.

Hands-on experience with cloud platforms such as Azure, AWS, or Google Cloud, including infrastructure-as-code tools such as Terraform, ARM Templates, or Cloud Formation.

Experience building and maintaining ETL/data pipelines and working with multiple data formats and sources, including JSON, Parquet, relational databases, cloud databases, and messaging platforms.

Understanding of modern data architectures, including data lakes, lake houses, streaming pipelines, and object storage technologies such as ADLS Gen2 and Amazon S3.

Experience with SQL and No

SQL technologies such as Cosmos DB, MongoDB, Cassandra, or Redis, with the ability to analyze data and translate findings into meaningful insights.

Experience with containerization and orchestration technologies such as Docker and Kubernetes.

Familiarity with Dev Ops and software development practices, including Git, CI/CD, build tools, code quality tools, and artifact management.

Experience with digital data and customer-data technologies such as Adobe Experience Platform, Real-Time CDP, Journey Analytics, tag management platforms, SDKs, and REST APIs.Understanding of data privacy, security, governance, and regulatory considerations, including GDPR.Experience working in Agile environments using Scrum, SAFe, or similar methodologies.

Strong problem-solving and troubleshooting skills, with the ability to collaborate across technical and business teams.

Strong written and verbal communication skills, with the ability to explain technical concepts and data insights clearly.

Additional Knowledge and Skills Working effectively within an AI enabled environment:  Ability to use AI tools (e.g., Microsoft Copilot) to support daily work…