GDSP is seeking a Data Engineer to own the data infrastructure that unlocks deal intelligence and automation capabilities across the organization. The role is responsible for designing, building, and maintaining the pipelines, data models, and platforms that enable deal teams to access precise, reliable insights from broad data sets (deal telemetry, pricing models, customer usage, pipeline signals) at scale, with speed and accuracy that hold as volume grows. These data products serve deal strategists, pricing leaders, and senior executives who depend on them to structure, evaluate, and negotiate transformative contracts with AWS's most strategic customers. Quality, freshness, and accuracy of data outputs have direct, measurable impact on deal velocity, pricing quality, and revenue outcomes. This is a high-visibility role. The data engineer partners closely with product management to translate analytical requirements into scalable data solutions, and with engineering teams to ensure pipelines integrate cleanly across GDSP's tooling ecosystem. The role will leverage generative AI and AWS services to raise the bar on how GDSP consumes and acts on data. Key job responsibilities- Build and maintain backend data infrastructure for analytical and visualization platforms, ensuring data is clean, fresh, and optimized for downstream consumption- Translate business problem statements into technical data requirements, partnering with product management and stakeholders to define what data products to build- Automate and optimize reporting processes to enable self-service analytics at scale, reducing manual effort and improving speed to insight- Develop measurement frameworks and metrics that quantify deal execution performance and operational health- Ensure data quality through monitoring, validation, auditing, and documentation of pipelines and data sources- Leverage AWS services and generative AI to build next-generation data solutions that improve efficiency and unlock new analytical capabilities. About the team. The team builds products that power how GDSP operates. Reporting and tooling that are held to the highest standards of clarity, reliability, and scalability. Diverse Experiences. AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasnt followed a traditional path, or includes alternative experiences, dont let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the worlds most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating thats why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture. AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. Mentorship & Career Growth. Were continuously raising our performance bar as we strive to become Earths Best Employer. Thats why youll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/ Life Balance. We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, theres nothing we cant achieve. Basic Qualifications- 5 years of data engineering experience- Experience with data modeling, warehousing and building ETL pipelines- Experience with SQL- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or Node. JS- Experience mentoring team members on best practices. Preferred Qualifications- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR- Experience operating large data warehouses.