Data Engineer

GM Financial•Arlington, TX

7d•Hybrid

About The Position

GM Financial is the wholly owned captive finance subsidiary of General Motors, headquartered in Fort Worth, and a global provider of auto finance solutions. This Data Engineer role designs, integrates, and manages data pipelines that collect, transform, and monitor data from internal and external sources to support business and analytics needs. The position works with business stakeholders, data engineering teams, and privacy compliance partners to implement scalable data solutions, improve data processes, and assess emerging technologies to promote innovation and operational efficiency. The role contributes to research and experimentation with batch and streaming data technologies, supports adoption of emerging technologies, establishes business rules and data schema enhancements, monitors data loads, and helps resolve data availability and activation issues. It also coordinates with Privacy Compliance to support proper data collection and handling. The position requires a high school diploma or equivalent and a bachelor’s degree in a related field or equivalent experience. Data engineering experience is preferred. The work environment is flexible hybrid, with two days per week in the office. Candidates who require visa sponsorship now or in the future cannot be considered.

Requirements

High school diploma or equivalent required.
Bachelor’s degree in a related field or equivalent experience required.
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 CloudFormation.
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, lakehouses, streaming pipelines, and object storage technologies such as ADLS Gen2 and Amazon S3.
Experience with SQL and NoSQL technologies such as Cosmos DB, MongoDB, Cassandra, or Redis, and ability to analyze data and translate findings into meaningful insights.
Experience with containerization and orchestration technologies such as Docker and Kubernetes.
Familiarity with DevOps 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.
Ability to use AI tools, such as Microsoft Copilot, to support daily work.
Ability to evaluate AI outputs for accuracy, compliance, and bias.
Experience integrating AI into workflows to improve efficiency or insights.
Familiarity with AI-assisted research, summarization, and content generation.
Understanding of responsible AI use, including ethics and data protection.

Nice To Haves

Experience with data engineering preferred.

Responsibilities

Work with internal business partners to identify, capture, collect, and format data from external sources, internal systems, and the data warehouse to extract features of interest.
Contribute to evaluation, research, and experimentation with batch and streaming data engineering technologies in a lab to keep pace with industry innovation.
Work with data engineering groups to inform them about and showcase emerging technology capabilities and enable adoption of new technologies and associated techniques.
Coordinate with Privacy Compliance to ensure proper data collection and handling.
Create and implement business rules and functional enhancements for data schemas and processes.
Monitor data loads and resolve related issues.
Work with internal business clients to troubleshoot data availability and activation issues.