Senior Data Engineer

Job Description

Hybrid: Reporting to a specific location where work needs to be performed / collaboration should happen (regardless this location is within a hub) and without regard to where a candidate lives (some employees may end up hybrid who live more than 50 miles away but where business needs dictate them to be onsite at a specific location three days per week).

• This role is categorized as hybrid. This means the successful candidate is expected to report to GM Georgia IT Innovation Center or GM Global Technical Center or Austin Innovation Technical Center or Mountain View Technical Center  three times per week, at minimum.
• This job is eligible for relocation benefits.

ABOUT THE ROLE:

As a Senior Data Engineer, you will play a critical role in building scalable
data pipelines and industrialized data assets focused on CRM data
integration and optimization to drive sales and marketing strategies
across our automotive business. You will partner with Data Scientists, BI
Developers, and Marketing and Sales Operations teams to deliver data
solutions that enhance customer engagement, improve lead conversion, and
support targeted campaigns. By leveraging Azure Databricks and other
cloud-native technologies, you’ll enable advanced analytics on customer
behavior, dealer interactions, vehicle purchase trends, and campaign
performance — all key to shaping the future of automotive retail and
marketing. Are you ready to engineer the data backbone that powers
smarter decisions and better customer experiences?

RESPONSIBILITIES:

• Develop and manage data pipelines on Azure Databricks, integrating data from various sources into a unified, high-performance platform
• Communicate and maintain Master Data, Metadata, Data Management Repositories, Logical Data Models, and Data Standards
• Design and implement large-scale data engineering solutions using Spark on Azure Databricks that meet functional and non-functional business requirements
• Collaborate with stakeholders to identify opportunities for automation and efficiency, including pipeline orchestration and monitoring
• Build industrialized analytic datasets and delivery mechanisms that utilize modern data engineering practices to deliver actionable insights into customer acquisition, operational efficiency, and other key metrics
• Implement and maintain Delta Lake for optimized performance, reliability, and ACID-compliant data storage
• Work closely with business and analytics teams to translate business needs into scalable, production-ready data solutions
• Create highly consistent and accurate analytic datasets suitable for BI tools and machine learning models
• Build and manage CI/CD pipelines for Azure Databricks using tools like GitHub Actions, Azure DevOps, or Jenkins to enable seamless deployment, data model versioning, and infrastructure automation

Additional Job Description

REQUIREMENTS:

• 10+ years of experience in data engineering, building large-scale data platforms
• 5+ Strong hands-on experience with Azure Databricks, Spark
  (PySpark/Scala), and Delta Lake for big data processing
• Proficiency in Python and SQL for data transformation and
  orchestration
• Solid experience with orchestration tools such as Airflow, Databricks
  Workflows, or Azure Data Factory
• Familiarity with CI/CD practices and tools such as GitHub Actions,
  Jenkins, or Azure DevOps
• Experience with Azure ecosystem (e.g., ADLS Gen2, Synapse, Key
  Vault, Azure Functions)
• Strong understanding of DevOps and Infrastructure as Code practices
  (Terraform, ARM templates, etc.)
• Familia