Data Engineer

GM Financial•Arlington, TX

8d•Hybrid

About The Position

GM Financial is a global provider of auto finance solutions and the wholly owned captive finance subsidiary of General Motors. This 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 Data Engineer 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. The position works with internal business partners to identify, capture, collect, and format data from external sources, internal systems, and the data warehouse. It also contributes to research and experimentation with batch and streaming data engineering technologies, supports adoption of emerging technologies, implements business rules and enhancements to data schemas and processes, monitors data loads, and helps resolve data availability and activation issues. The role requires knowledge of distributed data platforms, cloud services, data pipelines and architectures, databases, containerization, DevOps practices, digital data technologies, privacy and governance, and Agile methods. Experience with data engineering is preferred. A high school diploma or equivalent and a bachelor’s degree in a related field or equivalent experience are required. The work environment is described as flexible hybrid, with two days per week in the office. The position is not open to candidates who require visa sponsorship now or in the future.

Requirements

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 ability to collaborate across technical and business teams.
Strong written and verbal communication skills, with ability to explain technical concepts and data insights clearly.
Ability to use AI tools, such as Microsoft Copilot, to support daily work.
Skills evaluating 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.
High school diploma or equivalent required.
Bachelor’s degree in a related field or equivalent experience required.

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-related groups to communicate and showcase emerging technology capabilities and enable adoption of new technologies and 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.
Perform data load monitoring and resolution.
Work with internal business clients to solve data availability and activation issues.