Senior Data Engineer, AWS Analytics Engineering
A senior data engineer on AWS's internal analytics platform team owns 3-5 data domains end-to-end, operating hundreds of petabyte-scale ETL pipelines and designing data models and infrastructure that inform AWS leadership decisions. Core stack is SQL plus AWS data services (EMR, Glue, Redshift, Lambda), with an emphasis on AI/ML, LLMs, and agentic frameworks.
- Compensation
- Not disclosed
- Location
- Seattle, Washington, USA
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data engineering
- Posted
Skills
Must have
- Expert with SQL
- Expertise in ETL and data processing
- Experience with cloud-based data services such as AWS EMR, Glue, Redshift, and Lambda
- Hands-on experience with AI/ML technologies, including LLMs
- Strong understanding of designing and building Agentic Frameworks (autonomous agents, multi-agent orchestration, tool integration)
- Ability to own data and infrastructure architecture at the team level with security, scalability, and cost effectiveness
- Able to solve complex ambiguous problems and make technical trade-offs between short-term and long-term needs
- Ability to mentor and develop others, influence related teams' architecture, and build consensus
- Willingness to participate in on-call rotation and own operational health (monitoring, alarming, runbooks, SLA tracking)
- Comfort with ambiguity in a fast-paced environment
Full description
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 Senior Data Engineer to join our team. This individual will own 3-5 data domains end-to-end, operate hundreds of pipelines, and drive architectural improvements that impact how AWS leadership makes decisions. You will partner with service teams across AWS to design data contracts, build ingestion flows, and deliver analytical models that serve the entire organization.
The ideal candidate is a technical leader who thrives in ambiguity, takes a long-term architectural view, and consistently delivers exemplary solutions. You are an expert with SQL, ETL, and data processing, with experience leveraging cloud-based data services such as AWS EMR, Glue, Redshift, and Lambda. The candidate should have hands-on experience with AI/ML technologies, including LLMs, and a strong understanding of designing and building Agentic Frameworks — including autonomous agents, multi-agent orchestration, and tool integration. You are comfortable with ambiguity in a fast-paced environment, able to think big while paying careful attention to detail, and passionate about building data platforms using AI to accelerate the next generation of analytics at AWS scale.
Key job responsibilities
Identify limitations and opportunities in data processing tools, drive improvements and innovation, define data processing guidelines, and ensure best practices in all pipelines designed and reviewed. For example: redesigning ingestion frameworks to handle new AWS service telemetry data, or building reusable transformation patterns adopted across multiple teams.
Define and own data architecture at the team level — ensuring architecture effectively matches business problems and data challenges with security, scalability, and cost effectiveness. Show good judgment making technical trade-offs between short-term technology needs and long-term business needs.
Produce exemplary code — solutions that are easily usable by customers, inventive, secure, easily maintainable, appropriately scalable, and extensible. Build solutions that are easy for others to contribute to. Work to simplify, optimize, and remove bottlenecks.