Data Engineer, PXT Central Science

About the role

The Central Science Team within Amazon's People Experience and Technology org (PXTCS) uses economics, behavioral science, statistics, machine learning, and Generative AI to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, well-being, and the value of work to Amazonians. We are an interdisciplinary team, which combines the talents of science, engineering, and UX to develop and deliver solutions that measurably achieve this goal.

Requirements

• Knowledge of professional software engineering & best practices for full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence
• 3+ years of data engineering experience
• Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
• Experience with data modeling, warehousing and building ETL pipelines
• Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
• Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)
• Bachelor's degree or foreign equivalent in computer science, engineering, mathematics or equivalent
• Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
• 4+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
• Bachelor's degree or above in computer science
• Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
• Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
• Our inclusive culture empowers Amazonians to deliver the best res
• Cross-team Collaboration: Partner with economics, data science, and software engineering teams to translate analytical requirements into production-ready solutions; participate in technical design reviews and architecture discussions
• Analytics & Infrastructure: Maintain layered data systems used by economists and scientists; build automated reporting solutions; work across multiple interconnected AWS accounts with security best practices

Additional Information

The PXT Central Science team is looking for a Data Engineer. This individual will join a team of economists and scientists to own and accelerate science and analytics in our rapid employee intelligence workstream. This suite of models identifies causal factors driving changes in employee sentiment, actions, and business outcomes. Key job responsibilities PXTCS is looking for a data engineer with expertise in complex data environments. You will be responsible for enhancing our existing data architecture to further standardize metrics and definitions, building and testing new features, developing end-to-end data engineering solutions for complex analytical problems, and collaborating with economists, data scientists, and software engineers to translate data into actionable insights. Specific responsibilities include: - Data Pipeline Development: Design and maintain scalable data pipelines using native AWS services (Glue, EMR, Lambda); build monitoring and error handling for data workflows; optimize performance, reliability, and cost efficiency - Model Productionization & API Development: Develop and maintain APIs and data serving layers that productionize science models for downstream consumption; build batch and real-time inference pipelines - Data Integration & Quality: Build scalable feature extraction and processing frameworks for diverse data types; develop robust data quality and validation checks; create flexible schemas supporting evolving requirements - Cross-team Collaboration: Partner with economics, data science, and software engineering teams to translate analytical requirements into production-ready solutions; participate in technical design reviews and architecture discussions - Analytics & Infrastructure: Maintain layered data systems used by economists and scientists; build automated reporting solutions; work across multiple interconnected AWS accounts with security best practices

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