Amazon.com Services LLC
Human Resources, Data Engineering, global corporate
SeniorDataEngineer,PeopleExperienceandTechnologyCentralScience
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“Senior Data Engineer, People Experience and Technology Central Science at Amazon.com Services LLC. Skills: Data engineering, Data architecture, ETL, Machine learning. Design data pipelines. Build data pipelines”
What You'll Achieve.
Enhance employee experience; Enable data-driven decisions; Support advanced analytics; Support reporting; Support machine learning applications; Ensure accuracy; Ensure reliability; Solve complex workforce challenges; Enhance performance; Enhance scalability; Enhance security
Industry & Context.
Analytical problems; Complex problems
What They're Looking For.
Must Have
5+ years data engineering experience, Experience data modeling, Experience building ETL pipelines, Experience mentoring team members, Bachelor's degree in computer science, Bachelor's degree in engineering, Bachelor's degree in analytics, Bachelor's degree in mathematics, Bachelor's degree in statistics, Bachelor's degree in IT
Nice to Have
Experience with big data technologies, Experience operating large data warehouses, 7+ years workflow experience, Master's degree in Computer Science, Master's degree in Computer Engineering, Master's degree in Information Technology, Master's degree in related fields
What You'll Do.
Design data pipelines
Maintain data pipelines
Design infrastructure
Maintain infrastructure
Standardize definitions
Architect data engineering solutions
Deliver data engineering solutions
Productionize science models
Develop ETL processes
Maintain ETL processes
Implement data quality controls
Implement monitoring systems
Productionize machine learning models
Own data serving layers
Understand business requirements
Translate requirements to solutions
Mentor junior engineers
Establish best practices
Define data architecture decisions
Lead technical design reviews
Set technical direction
Influence data strategy
Influence modeling approaches
Drive operational excellence
Implement security best practices
Design scalable solutions
How You'll Work.
Team & Collaboration
Collaborate with data scientists; Collaborate with software engineers; Collaborate with BI engineers; Collaborate with HR business partners; Collaborate with economists; Cross-functional collaboration
Full Job Description
As a Senior Data Engineer on the PXT Central Science team at Amazon, you will be part of the Data team that builds the foundational data infrastructure and analytics tools powering PXTCS. We create secure, scalable solutions that enable teams across PXTCS to access and analyze data effectively while maintaining data integrity. In this role, you will design, build, and maintain scalable data pipelines and infrastructure that drive our people analytics capabilities. You will collaborate with data scientists, software engineering teams, business intelligence engineers, and HR business partners to transform complex HR data into actionable insights that shape Amazon's people strategy. Your technical expertise will help enable data-driven decisions that enhance the employee experience across Amazon's global workforce. Key job responsibilities PXTCS is looking for a Senior Data Engineer with deep expertise in complex data environments. You will own and drive the evolution of our data architecture to standardize metrics and definitions, architect and deliver end-to-end data engineering solutions for complex analytical problems, productionize science models for downstream consumption, and lead cross-functional collaboration with economists, data scientists, and software engineers to translate data into actionable insights. Specific responsibilities include: • Design, develop, and maintain efficient ETL processes and data pipelines to collect, process, and integrate data from multiple HR systems and sources using native AWS services (Glue, EMR, Lambda) • Build and optimize data models that support advanced analytics, reporting, and machine learning applications • Implement data quality controls and monitoring systems to ensure accuracy and reliability of people data across pipelines and production endpoints • Partner with data scientists and software engineering teams to productionize machine learning models that solve complex workforce challenges, including owning API archit
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