Data Engineer II, AWS Analytics Engineering - FDT

The AWS Analytics Engineering (AAE) organization is the analytics backbone of AWS — we build and operate the data platform that powers business decisions across more than 150 AWS services. Every insight surfaced to AWS product leadership, from service adoption trends to revenue drivers, flows through systems our team designs, builds, and maintains. We operate at massive scale — processing petabytes of data daily through thousands of jobs consisting of transformations, reporting queries, ingestions, and infrastructure management scripts. Our engineers work directly with source systems to procure data, convert it into structured formats, build large-scale processing pipelines, design analytical data models, and maintain infrastructure with the highest security and compliance standards. We are seeking a Data Engineer to join our team. This individual will be a significant and autonomous contributor, owning a major portion of the team's data architecture — solving difficult problems, building logical data models, and delivering data pipelines that are stable, performant, and consistently high quality. You will work with engineers and stakeholders across AWS to design data contracts, build ingestion flows, and deliver analytical models that increase self-service access to datasets and business effectiveness. The ideal candidate applies appropriate technologies and best practices, writes pragmatic and maintainable code, and takes ownership of ongoing data quality. You are proficient with SQL, ETL, and data processing, with experience using cloud-based data services such as AWS EMR, Glue, Redshift, and Lake Formation. The candidate should have exposure to AI/ML technologies, including LLMs, and a foundational understanding of Agentic Frameworks — including autonomous agents, multi-agent orchestration, and tool integration. You are trusted with autonomy in ambiguous environments where data design is not well defined, able to balance customer requirements with team priorities, and passionate about building data platforms using AI to accelerate the next generation of analytics at AWS scale. About AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Key job responsibilities Identify and resolve data quality issues in processing tools, contribute to improvements and innovation, and ensure best practices in pipelines you design and maintain. For example: optimizing ingestion flows for new data sources, or building efficient transformation patterns within the team's domain. Build and optimize logical data models and data pipelines for difficult datasets — ensuring solutions are testable, maintainable, and efficient while addressing security, scalability, and cost considerations. Make appropriate technical trade-offs at the dataset level, balancing pragmatic short-term decisions with sustainable long-term approaches. Produce high-quality code — solutions that are pragmatic, secure, maintainable, and flexible without over-engineering. Write code that engineers unfamiliar with the system can understand. Limit the use of short-term workarounds and minimize incidental complexity. Contribute to infrastructure decisions within the team's data architecture. Efficiently manage resources (system hardware, data storage, query optimization, AWS infrastructure) and build solutions that are stable and performant. Solve difficult problems — for example, designing data models that integrate multiple sources within the team's domain, or combining datasets to unlock new analytical capabilities. Identify issues that may lead to data inconsistency or gaps in data quality, and proactively resolve them. Break down project work into manageable tasks, deliver independently, and collaborate effectively with peers on shared dependencies. Resolve discordant views and build consensus among team members. Mentor peers, participate in hiring, and contribute to team knowledge-sharing Drive data engineering best practices within the team — code quality, data certification, dependency management, and operational excellence. Establish SLAs, automate manual processes, and improve self-service access to data. Drive improvements through code reviews, design discussions, team planning, and operational reviews. Participate in on-call rotation and take ownership of operational health for data systems you own — contribute to monitoring, alarming, runbooks, and incident resolution. - 5+ years of data engineering experience - 3+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience - 3+ years of developing and operating large-scale data structures for business intelligence analytics using SQL experience - 5+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience - 3+ years of developing and operating large-scale data structures for business intelligence analytics using OLAP technologies and Data Modeling Experience - Experience communicating with users, other technical teams, and management to collect requirements, describe data modeling decisions and data engineering strategy - 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) - Experience with big data technologies such as: Hadoop, Hive, Spark, EMR - Experience working with Data & AI related technologies, including, but not limited to, AI/ML, GenAI, Analytics, Database, and/or Storage - 4+ years of data warehouse technical architectures, data modeling, infrastructure components, ETL/ ELT and reporting/analytic tools and environments, data structures and hands-on SQL coding experience - Experience operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets, or experience with software development lifecycle Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. The base salary range for this position is listed be