Senior Data Engineer, Execution and Planning Science (EPS)

Amazon.com, Inc.•Austin, TX

26d

About The Position

Amazon is looking for a Senior Data Engineer to help develop a data strategy for Science-Tech Products for NA Supply Chain organization within the Execution and Planning Science (EPS) team. This role will be critical in implementing AI-driven transformation of supply chain planning processes, particularly focusing on under-the-roof labor planning initiatives. This Data Engineer will work cross-functionally with multiple business stakeholders, technology and Science teams and work with a team of talented scientists and SDEs to develop and implement AI agent-based solutions in the growing GenAI and Large Language Model (LLM) space that is business critical to keep innovating. The role involves creating and implementing a data engineering strategy to support specialized AI agents for Input Validation, Science Model Explainability, Data Analytics, Labor Plan Scenarios, and Risk Analysis capabilities in order to transform manual planning processes into intelligent, automated workflows. You will work to enable a roadmap that will enable automation of manual labor planning processes by enhancing agentic decisions via data-driven insights. Your focus will be on Science-Tech services that automate routine operational tasks while maintaining human oversight for critical decisions, driving substantial improvements in planning efficiency through intelligent scenario evaluation and selection.

Requirements

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 NodeJS
Experience communicating with users, other technical teams, and management to collect requirements, describe data modeling decisions and data engineering strategy
Knowledge of distributed systems as it pertains to data storage and computing
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
Experience in development or technical support, or experience with data infrastructures: relational analytic DBMS, Elastic-Search, and Big Data EMR/EC2/Glue/Lambda

Nice To Haves

Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
Experience operating large data warehouses
Experience building data products incrementally and integrating and managing data sets from multiple sources
Experience providing technical leadership on high-impact cross-fucntional technical project
Experience working with Data & AI related technologies, including, but not limited to, AI/ML, GenAI, Analytics, Database, and/or Storage
Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
Experience building multi-agent systems, LangChain/LangGraph applications, or custom AI agent frameworks
Experience with orchestration tools (Airflow, Step Functions, MWAA) and AI-powered workflow automation
Experience with infrastructure-as-code (CDK, Terraform, CloudFormation) and AI-assisted infrastructure management

Responsibilities

Work cross-functionally with multiple business, engineering, and leadership stakeholders to deliver labor planning products that are business critical for supporting network growth and program expansion in Amazon's increasingly complex last mile operations.
Deep diving current state processes and systems, understanding operational challenges within those processes and systems, collaborating cross-functionally to brainstorm and drive the right solution for the business.
Collaborating with cross-functional partners to understand pain points and prioritize projects, initiatives, and product solutions
Collaborating regularly and independently with tech teams to define customer acceptance criteria, test new features and drive the roll-out and adoption by business.
Making trade-off decisions with the tech team regarding features and priorities within the roadmap based on impact and speed to market.
Managing the data engineering strategy from high level organizational design to tactical execution.
Disambiguating product requirements based on an intimate knowledge of the data technologies available.