Data Engineer III

Why GMF?

GM Financial International Operations conducts business in Latin America and China. International Operations offers a wide range of wholesale and retail automotive financing products to dealers and their customers. The strength of the international business lies in its ability to tailor its products to meet unique dealer needs, consumer preferences, and national requirements in local markets. Latin American country operations include Brazil, Chile, Colombia, Mexico, and Peru.

Responsibilities

About the role

Data Engineer III designs, develops, tests, deploys, and maintains highly scalable data management and integration solutions. The role is responsible for designing and developing complex, production-grade data pipelines, transforming data from diverse enterprise sources into reliable and consumable data products, and ensuring data quality, performance, security, scalability, and operational reliability.

This role requires advanced hands-on experience with Databricks and modern cloud data engineering technologies, with the ability to independently design, implement, optimize, and support complex data engineering solutions. Strong experience with cloud services and architecture, particularly Microsoft Azure, is highly desirable.

About the initiative

This role will be part of a strategic, multi-year transformation focused on modernizing GM Financial’s servicing platform and shaping the future of how we serve our customers. The initiative brings together business, technology, data and customer experience teams to deliver simpler, more scalable and innovative capabilities, leveraging product and service design, automation and AI. Team members will have the opportunity to contribute to a high-impact transformation, collaborate across functions and markets, and help build capabilities that create lasting value for our customers and the business.

In this role you will:

• Design, develop, test, deploy, optimize, and support complex, scalable, and production-grade data pipelines using Databricks and related data engineering technologies.
• Develop robust ETL/ELT solutions to ingest, transform, integrate, and deliver data across cloud and enterprise environments.
• Design reusable and maintainable data integration patterns leveraging Databricks, Apache Spark/PySpark, Python, SQL, Delta Lake, APIs, and other appropriate technologies.
• Design and maintain efficient data models, data structures, and storage solutions.
• Implement data quality, validation, reconciliation, monitoring, and error-handling processes to ensure reliable and accurate data delivery.
• Optimize Databricks and Spark workloads for performance, scalability, reliability, and cost efficiency.
• Collaborate with architects, application teams, data analysts, data scientists, and business stakeholders to translate complex data requirements into scalable technical solutions.
• Integrate data from diverse enterprise sources using batch, API, streaming/event-driven, and other appropriate integration patterns.
• Establish and maintain appropriate data security, access control, and governance practices.
• Apply software and data engineering best practices, including source control, automated testing, CI/CD, monitoring, logging, and production support.
• Analyze, troubleshoot, and resolve complex data integration, performance, and production issues.
• Document data processes, data flows, integration patterns, technical designs, pipeline architectures, and operational procedures.
• Perform other duties as assigned.
• Conform with all company policies and procedures.

Qualifications

What makes you an ideal candidate?

• Advanced hands-on experience with Databricks, Apache Spark/PySpark, Delta Lake, Python, and SQL.
• Strong experience designing, developing, optimizing, and supporting complex, production-grade data pipelines using Databricks.
• Strong knowledge of ETL/ELT architecture, distributed data processing, data integration, and pipeline orchestration.
• Strong understanding of cloud-based data architectures and services, with Microsoft Azure experience strongly preferred.
• Experience with cloud data storage, integration, security, monitoring, identity/access management, and deployment capabilities.
• Experience ingesting and processing data from multiple formats and sources, including XML, JSON, Parquet, APIs, relational databases, cloud data stores, and messaging/event platforms.
• Strong experience with API-based integrations and enterprise application integrations.
• Knowledge of data modeling, data quality, reconciliation, metadata management, security, and data governance concepts.
• Experience with performance tuning and troubleshooting of large-scale distributed data workloads.
• Knowledge of source control, automated testing, CI/CD, and modern DevOps/DataOps practices.
• Working knowledge of Agile development, Scrum, and Application Lifecycle Management.
• Experience with Microsoft Azure technologies such as Azure Data Lake Storage Gen2 (ADLS Gen2), Azure Data Factory, Event Hubs, Key Vault, and Azure DevOps is preferred.
• Experience with Databricks Workflows/Jobs and Unity Catalog is preferred.
• Experience with Kafka or other event-streaming technologies is preferred.
• Experience integrating Oracle and other enterprise relational databases is preferred.
• Knowledge of IBM InfoSphere DataStage and Unix scripting is preferred.
• Experience modernizing legacy data integration platforms and workloads into modern cloud-based data architectures is preferred.

Skills

• Databricks
• Apache Spark / PySpark
• Python Programming
• SQL
• Delta Lake
• Complex Data Pipeline Design and Development
• ETL / ELT and Data Integration
• Cloud Data Engineering
• Microsoft Azure
• Data Modeling
• API Integration
• Big Data and Distributed Processing Technologies
• Data Quality and Reconciliation
• Data Security and Governance
• Performance Optimization
• CI/CD and DevOps/DataOps
• Monitoring and Production Support
• Technical Troubleshooting and Root Cause Analysis

Additional Knowledge and Skills

Working effectively within an AI enabled environment:

• Ability to use AI tools (e.g., Microsoft Copilot) to support daily work
• Skills 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

Experience & Education

• Bachelor's degree in computer science, Information Systems, Engineering, Data Engineering, or a related field, or equivalent professional experience required.
• 4-6 years of experience in data engineering or a related field, including experience designing and developing complex, production-grade data pipelines and integration solutions, required.
• Demonstrated advanced hands-on experience with Databricks, including the design, development, optimization, and operational support of complex data pipelines, required.
• Demonstrated experience with Apache Spark/PySpark, Python, SQL, and large-scale distributed data processing required.
• Experience with modern cloud-based data platforms and architectures required.
• Experience with Microsoft Azure cloud services and cloud data architectures is strongly preferred.
• Experience integrating data from multiple enterprise systems, APIs, relational databases, cloud storage platforms, and messaging/event platforms preferred.
• Experience with data platform modernization or migration from legacy ETL technologies to modern cloud data platforms preferred.

GM Financial is an Equal Opportunity Employer. We do not discriminate against any applicant or employee based on race, color, age, gender, marital status, national origin, religion, sexual orientation, gender identity, gender expression, veteran status, disability, or any other that undermines human dignity and aims to nullify or impair people’s rights and freedoms.

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