Staff Data Engineer, Activation Science

General Motors - Little Rock, AR

Full-time - Mid Level

Remote - Little Rock, AR

Transportation Equipment Manufacturing

About the position

The Marketing Applied Sciences team at General Motors is dedicated to developing analytics-driven solutions that empower various GM organizations to achieve their business objectives. As a member of this multi-disciplinary team, you will engage in the design, development, and deployment of analytic models that support business-facing analytic groups. The focus is on both advanced analytics strategy and applied, project-based solutions, particularly in the company's most critical business areas. In your role as a Data Engineer, you will be responsible for building analytical data sets that support Advanced Analytics projects. This involves close collaboration with innovative Researchers and Data Scientists to deliver value aligned with GM's vision for the future. The position reports to the Product Owner of Marketing Activation Science, providing visibility to the broader Enterprise Data, Analytics, and Insights organization, which is committed to democratizing data and decision-making across the company. Your contributions will include collaborating with internal and external stakeholders, driving the adoption of cloud-first technologies, adhering to standards for data product validation and monitoring, and ensuring seamless integration of data engineering initiatives across various teams. Staying abreast of emerging trends and technologies in Data Engineering will be crucial, as will fostering a culture of continuous learning and knowledge sharing within the team and the wider data engineering community.

Responsibilities

• Collaborate with internal and external stakeholders, including Activation Data Science, to deliver on the portfolio.
• Drive the adoption of cloud-first technologies and industry-standard Data Engineering practices to accelerate and scale engineering capabilities.
• Adhere to standards and processes to validate, monitor, and support data products that enable the decision science portfolio and data science capabilities.
• Collaborate with cross-functional teams, including data governance, security, data architecture, release management, Dev & ML Ops, and infrastructure, to ensure seamless integration and alignment of data engineering initiatives.
• Stay up to date with emerging trends and technologies in the field of Data Engineering, proactively identifying opportunities for improvement and innovation within the organization.
• Participate in a culture of continuous learning, knowledge sharing, and development within the team and the broader data engineering community.

Requirements

• 7+ years of hands-on experience developing and implementing enterprise-scale data and analytics solutions using modern hybrid cloud technologies with a focus on data pipelines and reporting.
• Bachelor's degree (or equivalent work experience) in Computer Science, Data Science, Software Engineering, or a related field; Master's degree is a plus.
• Strong problem-solving and analytical skills with practical experience analyzing and curating large scale customer event data products from disparate data sources including 1st party and 3rd party data and map type data with variable structure.
• In-depth knowledge of industry-standard Data Engineering practices including data privacy & security, ETL/ELT, data architecture, data quality assurance, performance optimization, source code management, release management, and operations.
• Expert programming skills in data and analytics platforms, big data processing frameworks and languages, and development tools including Azure (or similar), Databricks, Spark, Python, SQL, and GitHub.
• Effective communication and people skills, with the ability to collaborate effectively with cross-functional technical teams and non-technical stakeholders.
• Naturally curious with the ability to work independently and proactively.
• Experience working in an Agile development environment.
• Experience deploying machine learning models and process automation.

Nice-to-haves

• Experience with additional programming languages or data processing frameworks.
• Familiarity with data visualization tools and techniques.
• Knowledge of machine learning algorithms and their applications in data engineering.

Benefits

• Medical, dental, and vision insurance options.
• Health Savings Account and Flexible Spending Accounts.
• Retirement savings plan with company and matching contributions to 401K.
• Sickness and accident benefits.
• Life insurance coverage.
• Paid vacation and holidays, including parental leave for mothers, fathers, and adoptive parents.
• Tuition assistance programs and student loan refinancing options.
• Employee assistance program.
• Discounts on GM vehicles for employees, family, and friends.