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Sr. Data Engineer, WWFE - Analytics, Reporting, Operations and Governance
Sr. Data Engineer, WWFE - Analytics, Reporting, Operations and Governance-March 2024
Seattle
Mar 29, 2026
About Sr. Data Engineer, WWFE - Analytics, Reporting, Operations and Governance

  Amazon Web Services (AWS) is obsessed with ensuring the success of our customers. To this end, AWS is building the industry's best-trained sales force. As the number of AWS sellers and cloud services continues to expand rapidly, AWS is re-investing how to train and enable its sellers. AWS's Worldwide Field Enablement (WWFE) organization is driving this transition by pioneering highly innovative, multi-modal enablement that is able to support our field's diverse learning styles.WWFE is looking for a results-oriented Sr. Data Engineer to help AWS build our next generation enablement data management solution. WWFE's data solutions helps us to understand 1/ what training each of AWS's >20K sellers consume every day and 2/ how this training impacts their business results and productivity.Key job responsibilitiesThe Senior Data Engineer will:Collaborate with WWFE's business leaders and economists to identify data engineering requirementsLead the technical design of WWFE's reporting, analysis and data management solutionsCollaborate with AWS's Business Technology & Solutions team to leverage best practices and partner on solution designsLead and coach junior data engineers on the best practicesBuild robust and scalable data integration (ETL) pipelines using SQL/EMR/PythonWork with other AWS Region Services Business Intelligence Teams to integrate into existing data warehouses and plan for scaling into data lake solutionsDesign, develop and maintain scaled, automated, user-friendly systems that supports Business Intelligence Engineers to build reports and dashboardsImprove overall data architecture using innovative approaches and new processes from AWS stack or open sourceRecognize and adopt best practices in reporting and analysis: data integrity, test design, analysis, validation, and documentationA day in the lifeInclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have twelve employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon's culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.About the teamHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and we host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon's culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.We are dedicated to supporting our new team members. Our team has a broad mix of experience levels and Amazon tenures, and we're building an environment that celebrates knowledge sharing and mentorship.Our team also puts a high value on work-life balance. Striking a healthy balance between your personal and professional life is crucial to your happiness and success here, which is why we aren't focused on how many hours you spend at work or online. Instead, we're happy to offer a flexible schedule so you can have a more productive and well-balanced life-both in and outside of work.We are open to hiring candidates to work out of one of the following locations:Arlington, VA, USA | Seattle, WA, USA- 7+ years of data engineering experience- Experience building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data set

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