Staff Data Engineer – GPSC Insights Engineering

Job Description

This role is categorized as hybrid. This means the successful candidate is expected to report to Austin, TX three times per week, at minimum [or other frequency dictated by the business if more than 3 days].

The Role

Join our GPSC Insight Engineering team, responsible for ideating, incubating, and delivering new data solutions for global purchasing supply chain (GPSC) and our partners. As a Staff Data Engineer, you’ll be a technical lead for a small team of data engineers in designing and building scalable, high-performant data solutions to support the increasing needs of the GPSC organization.

As a senior level Data Engineer, you will design and build industrialized data assets and pipelines to support Business Intelligence and Advanced Analytics objectives. In this senior-level technical leadership role, you’ll bring a passion for quality, efficiency, and reliability, along with proven experience leading complex data engineering initiatives from concept to production. You will work in a highly collaborative environment across databases, streaming technology, CI/CD, cloud platforms, and modern data engineering tools—to create large, complex data sets that meet both functional and non-functional business requirements.

Beyond strong data engineering skills, you should have a solid foundation in modern software engineering principles—including code quality, design patterns, testing, and CI/CD—to deliver robust, maintainable, and production-ready systems. The ideal candidate combines a data-driven mindset with a strong understanding of business priorities, demonstrating creativity, sound decision-making, and the ability to influence and collaborate across teams.

What You'll Do

• Provide technical leadership for complex data engineering initiatives from concept through production, ensuring solutions are high-quality, efficient, and reliable.

• Assemble large, complex data sets that meet both functional and non-functional business requirements.

• Identify, design, and implement process improvements, including automation, data delivery optimization, and redesign for greater scalability.

• Architect, build, and optimize highly scalable data pipelines that incorporate complex transformations and efficient, maintainable code.

• Design and develop new source system integrations from a variety of formats including files, database extracts, and APIs.

• Lead and deliver data-driven solutions across multiple languages, tools, and technologies, contributing to architecture discussions, solution design, and strategic technology adoption.

• Develop solutions for delivering data that consistently meets SLA requirements and supports operational excellence.

• Partner closely with operations teams to troubleshoot and resolve production issues, ensuring platform stability.

• Drive engineering excellence by applying Agile methodologies, design thinking, continuous deployment, CI/CD best practices, and performance tuning strategies.

• Build tooling and automation to make deployments, production monitoring, and operational support more repeatable and efficient.

• Collaborate with business and technology partners, providing strategic guidance, leadership, and coaching to influence outcomes and align with enterprise goals.

• Actively mentor peers and junior engineers, fostering a culture of learning, innovation, and continuous improvement, while educating colleagues on emerging industry trends and technologies.

• Represent the team in executive-level forums to communicate status, risks, opportunities, and the strategic value of data engineering initiatives.

Your Skills & Abilities (Required Qualifications)

• Bachelor’s degree in Computer Science, Software Engineering, or related field

• 10+ years of experience in data engineering, including Python or Scala, SQL, and relational/non-relational storage (ETL frameworks, big data processing, NoSQL)

• 5+ years of experience in distributed, petabyte-scale data processing with Spark and container orchestration (Kubernetes)

• Hands-on experience with real-time data streaming in Kubernetes and Kafka

• Expertise in performance tuning (partitioning, clustering, caching, serialization techniques)

• Proficiency with SQL, key-value datastores, and document stores

• Strong CI/CD expertise and best practices

• Background in data architecture and modeling for optimized consumption patterns

• Proven experience developing data models and schemas for efficient storage, retrieval, and analytics, with query performance optimization