> Markdown version of [/jobs/ext/4396333-data-engineer-l5-aws-gnd-pax-data-and-analytcis](https://www.wearedevelopers.com/jobs/ext/4396333-data-engineer-l5-aws-gnd-pax-data-and-analytcis). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer (L5) - AWS GND, PAX Data and Analytcis - **Company:** Amazon.com, Inc. - **Location:** Seattle, WA, United States - **Experience:** Experienced - **Salary:** $132,100.0 - $178,800.0 - **Contract:** Permanent contract - **Skills:** Query Performance, Java (Programming Language), Artificial Intelligence, Amazon Web Services, Amazon S3, Data Analysis, Big Data, Code Review, Information Systems, Databases, Data Architecture, Data Centers, Data Validation, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Stores, Data Systems, Distributed Systems, Graph Database, Identity and Access Management, Python (Programming Language), Node.Js, Standard Sql, Scala (Programming Language), Data Streaming, Scripting, Data Storage Technologies, Retrieval-Augmented Generation, Large Language Models, Apache Spark, Electronic Medical Records, Generative AI, Indexer, Information Technology, Data Lineage, Apache Flink, AWS Glue, Apache Kafka, Non-relational Database, Data Pipelines, Amazon Redshift, Programming Languages - **Published:** October 12, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=393f46aff75e299e ## About the Role * 3+ years of data engineering experience * 3+ years of data modeling experience * 3+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience * Experience with data modeling, warehousing and building ETL pipelines * Experience with SQL * Bachelor's degree or foreign equivalent in Computer Science, Engineering, Information Systems, Mathematics, or a related field * Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS, * 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) * Knowledge of batch and streaming data architectures like Kafka, Kinesis, Flink, Storm, Beam ## Description Are you passionate about building data infrastructure that powers real business processes and impacts investments at scale? We're looking for an experienced AI-Driven Data Engineer to design, build, and scale the data foundations - grounded in business context - that power Large Language Models (LLMs), analytics, and autonomous agentic workflows. In this role, you'll bridge the gap by collecting data from multiple sources and building high-quality pipelines that convert business processes into context, establishing a reusable data model. You'll set up appropriate quality controls and monitor and optimize pipeline throughput to deliver highly available, scalable data pipelines. You'll work across the full data lifecycle - from ingestion and transformation to modeling and delivery - partnering with software engineers to ensure the right business entities flow through our systems and are translated into data that scientists, BI engineers, and analysts can use for in-depth analysis and metric development. Beyond solving complex data challenges, this role offers the opportunity to own significant portions of our data architecture, drive improvements to data quality and governance, and directly influence how your team and its customers make data-driven decisions. You'll be at the center of realizing the impact of your work - delivering on the goals of the Global Data Center Capacity Delivery Team. Key job responsibilities * Design, build, and maintain production-grade ETL/ELT pipelines and data infrastructure, applying strong SQL and AWS tools like Glue, S3, Spark , Athena etc to extract, transform, and load data at scale. * AI/RAG Data Architecture: Build and optimize data models, vector schemas, and feature stores designed for Retrieval-Augmented Generation (RAG) and analytical workloads. * Data Quality & Observability: Implement automated data validation, freshness/anomaly monitoring, lineage tracking, and alerting tailored for downstream AI consumption. * Optimize data storage and query performance through partitioning, indexing, and resource-efficient design across databases and distributed systems. * Collaborate with analysts, data scientists, and engineering peers to gather requirements, prioritize work, and deliver curated datasets and self-service data products that provide clear business value. * Improve data processes by automating manual operations, implementing data quality and lineage tracking, and contributing to code reviews, design discussions, and engineering best practices. A day in the life You will start your day reviewing pipeline health and data quality metrics, then shift into building or refining data models that support your team's analytical needs. You might pair with a data scientist to design a data pipeline AI workflow, create and lead a design review to evaluate a new pipeline architecture, or mentor a teammate on data modeling best practices. You will regularly evaluate opportunities to leverage generative AI tools to augment your productivity and streamline repetitive tasks and pipelines About the team Our team is focused on delivering reliable, well-governed, data infrastructure that enables informed decision-making across the organization. We value collaboration, technical rigor, and a commitment to making data accessible and trustworthy for our customers. As we look ahead, we are investing in modern data practices and scalable solutions that will grow with Global AWS evolving needs. If you are excited about building data systems that matter and growing your career alongside a supportive, inclusive team, we would love to hear from you.