Data Engineer II-IO

JOB DESCRIPTION

Why GMF Technology?

Innovation isn't just a talking point at GM Financial, it's how we operate. From generative AI and cloud-native technologies to peer-led learning and hackathons, our tech teams are building real solutions that make a difference. We're committed to AI-powered transformation, using advanced machine learning and automation to help us reimagine customer interactions and modernize operations, positioning GM Financial as a leader in digital innovation within a dynamic industry.

Join us and discover a workplace where your ideas matter, your development is prioritized, and you can truly make a global impact.

Flexible hybrid work environment (onsite 2 days/week 3 days remote) in Las Colinas/Irving, TX or Detroit, MI.
Please note: We are unable to provide any type of visa sponsorship for this position now or in the future.

We are seeking a skilled and innovative Data Engineer II to join our team and contribute to the advancement of our data engineering capabilities. This role involves working with cutting-edge technologies in both batch and streaming data environments, supporting the adoption of emerging tools and practices, and collaborating across teams to deliver high-quality data solutions.

RESPONSIBILITIES

• Research, evaluate, and experiment with emerging data engineering technologies in a lab environment to stay ahead of industry trends.
• Collaborate with data engineering teams to demonstrate the capabilities of new technologies and facilitate their adoption.
• Help define and refine processes and best practices for the data engineering discipline.
• Partner with business stakeholders, data architects, ETL developers, reporting teams, data scientists, and IT counterparts to identify and integrate data from various sources including external systems, internal databases, and data warehouses.
• Develop, test, deploy, monitor, and troubleshoot data pipelines and automation processes.
• Maintain comprehensive documentation and ensure reconciliation of data processes.

• Bachelor's degree in related field or equivalent work experience.
• 2-4 years of hands-on experience in data engineering.
• 2-4 years of experience processing large datasets using tools such as Databricks, Python, Spark, Kafka, and APIs.
• Proficiency in ingesting diverse data formats (XML, JSON, Parquet, SequenceFile) and integrating with various data sources (Oracle, cloud databases, Event Hub, Service Bus).
• Experience with cloud platforms (Azure, AWS) and infrastructure automation tools (ARM Templates, Terraform, CloudFormation).
• Strong understanding of cloud computing models (IaaS, PaaS, SaaS) and hybrid cloud environments.
• Familiarity with object storage technologies (Data Lake Storage Gen2, Blob, S3, ADLS).
• Experience with Agile/Scrum methodologies and application lifecycle management.
• Proficient in source control (Git), CI/CD pipelines (Azure DevOps), and artifact repositories (Artifactory).
• Experience creating and maintaining ETL processes.
• Knowledge of IT governance, privacy compliance, and GDPR.
• Preferred experience with Adobe Experience Platform, REST APIs, IBM Infosphere (Datastage), and Unix scripting.