Data Engineer II

GM Financial•Arlington, TX

13h•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. The Data Engineer II designs, integrates, and manages data pipelines that collect, transform, and monitor data from internal and external sources to support business and analytics needs. The role collaborates with business stakeholders, data engineering teams, and privacy compliance partners to implement scalable data solutions, improve data processes, and evaluate emerging technologies that support innovation and operational efficiency. The position contributes to efforts involving ingestion and processing of large data sets, including semi-structured and unstructured data. Its focus includes enabling data science and search-based applications on large, low-latency data sets in batch and streaming contexts. The role also supports data availability and activation, data load monitoring, and the development of business rules and functional enhancements for data schemas and processes. The position requires 2–4 years of hands-on data engineering experience, a high school diploma or equivalent, and a bachelor’s degree in a related field or equivalent experience. GM Financial offers a flexible hybrid environment with two days per week in the office. Candidates who require visa sponsorship now or in the future will not be considered.

Requirements

2–4 years of hands-on data engineering experience.
High school diploma or equivalent.
Bachelor’s degree in a related field or equivalent experience.
Experience designing, processing, and managing large-scale data using Hadoop, Spark, Kafka, HDFS, or similar distributed platforms.
Hands-on experience with 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 data formats and sources including JSON, Parquet, relational databases, cloud databases, and messaging platforms.
Understanding of data lakes, lakehouses, streaming pipelines, and object storage such as ADLS Gen2 and Amazon S3.
Experience with SQL and NoSQL technologies such as Cosmos DB, MongoDB, Cassandra, or Redis, and the 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 and ability to collaborate across technical and business teams.
Strong written and verbal communication skills, including the ability to explain technical concepts and data insights clearly.
Ability to use AI tools such as Microsoft Copilot to support daily work.
Skill in 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.

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 setting to keep pace with industry innovation.
Work with data engineering groups to communicate 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 issues.
Work with internal business clients to troubleshoot data availability and activation issues.