Are you passionate about sustainability and waste reduction efforts across Amazon's global organization?
Are you interested in building the data foundation that drives solutions balancing packaging and automation to increase speed, reduce cost, and ultimately delight our customers?
The Robotics Delivery and Packaging Innovation team is looking for a Data Engineer to join us in building and maintaining the end-to-end data pipelines and infrastructure that power our analytics and business decisions.
This is your opportunity to be a core part of the team that reduces waste for our business and our customers.
In this role, you will design, build, and operate the data pipelines behind our "source of truth" data store and metrics for warehouse automation and customer packaging.
You will develop robust, scalable
ETL/ELT solutions that integrate large, complex data spanning many sources and data structures using AWS tools, enabling data-driven decisions on OEE, machine metrics, and product performance across our packaging automation, IB sortation, and other warehouse automation products.
We are looking for a builder with deep SQL proficiency, strong Python skills, and knowledge of data modeling and warehousing best practices.
You will apply rigorous data quality and data security practices, including validation, deduplication, PII handling, access controls, and monitoring, so that stakeholders can trust the data and sensitive operational data stays protected as it moves through our pipelines.
You'll partner closely with Business Intelligence Engineers, Data Scientists, Product, and Operations to translate raw data into reliable, well-governed datasets and actionable insights.
The ideal candidate thrives amid ambiguity, builds for the future while delivering today, and understands the importance of code development best practices.
If you love hard problems with many solutions and a diverse, results-oriented environment, come join us!
Key job responsibilities
• Be hands-on with ETL/ELT to build and maintain reliable, scalable data pipelines using SQL, Python, and Spark that support automated reporting for warehouse automation and customer packaging
• Interface with other technology teams to extract, transform, and load data from a wide variety of sources spanning warehouse automation and customer packaging
• Implement data structures using best practices in data modeling, ETL/ELT processes, SQL, and Redshift, and manage our "source of truth" data stores
• Implement best practices in batch and real-time data processing to make pipelines performant
• Apply strong data quality practices (validation, deduplication, PII handling, drift detection, and monitoring) so consumers can trust the data
• Build data security and access controls into the data infrastructure, protecting sensitive operational and performance data throughout its lifecycle
• Model data and metadata for ad-hoc and pre-built reporting, and deliver high-quality datasets to support BIEs, Data Scientists, and customer reporting needs
• Manage and optimize cloud computing resources on AWS to support data infrastructure
• Use AI-assisted development tools (e.g., Amazon Q, Kiro) to accelerate pipeline development, SQL, and debugging, while validating generated output for correctness
• Create extensible, easy-to-maintain designs with a long-term vision, laying groundwork to support future machine learning and GenAI use cases
• Participate in strategic and tactical planning discussions A day in the life
As a Data Engineer on this team, you will own the end-to-end data pipelines and infrastructure that serve as our "source of truth" for warehouse automation and customer packaging metrics.
You will design, build, and operate robust and scalable
ETL/ELT solutions, integrating complex data from dozens of sources to enable data-driven decisions across the Robotics Delivery and Packaging Innovation Organization.
You will partner closely with Business Intelligence Engineers, Data Scientists, Software Engineers, Product Managers, and Operations teams to translate raw requirements into reliable and scalable data pipelines producing datasets that can be used and trusted across the org.
You will also champion data quality and security practices, ensuring stakeholders can trust the data while sensitive operational information stays protected throughout its lifecycle.