Job description

Do you want to build the products that help AWS ProServe and Frontier AI teams understand customer needs, develop the right solutions, and deliver them at scale? We are looking for a Data Engineer to own the data foundations behind the products and experiences that span ProServe, Frontier AI, and services teams within AWS Sales, Marketing and Global Sales. Your pipelines and datasets will let these teams deeply understand customer challenges, rapidly develop tailored solutions, and orchestrate delivery, directly accelerating how AWS delivers business outcomes to our customers. You will build and operate the pipelines and datasets that feed the context layer and agentic workflows: ingesting external insights, internal data, and insights collected by sales and delivery teams, and turning them into reliable, well-modeled datasets that models and agents can reason over. You design data models, ETL, and integration services that are flexible, extensible, and scalable, then support and grow them over time. This is a hands-on role where the playbook is written in real time. You ship to production and own the correctness of the data that downstream agents depend on. You are comfortable with ambiguity and partner with business leaders to build the pipelines that answer their key questions. You will unify fragmented data pipelines, build robust data models, and ensure reliable data lineage across multiple product areas, working closely with software engineers, product managers, and analytics teams. This is a role with high visibility to senior leadership and high opportunity for impact. Key job responsibilities - Own end-to-end delivery of data pipelines: design, build, test, deploy, and operate - Build and optimize ETL/ELT jobs that produce the foundational datasets powering the team's products and agentic workflows - Model structured and unstructured data (external insights, internal data, sales and delivery insights) so it is queryable and consumable by AI agents at scale - Design the data structures, schemas, and contracts that downstream services and agents depend on - Build and own data-quality tooling: validation, reconciliation, anomaly detection, and monitoring so bad data does not reach agents or models - Ship to production regularly in a high-ambiguity environment, balancing speed against reliability - Raise operational-excellence hygiene (data tests, monitoring, runbooks) for the pipelines you own - Write clear data models, pipeline design notes, and runbooks a peer can act on About the team ABOUT AWS: Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred 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. Why 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. 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 AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. Mentorship and 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. - 3+ years of data engineering experience - 1+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience - 1+ years of developing and operating large-scale data structures for business intelligence analytics using data modeling experience - Bachelor's degree or foreign equivalent in Computer Science, Engineering, Information Systems, Mathematics, or a related field - Experience writing and optimizing SQL queries with large-scale, complex datasets - 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) 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 amazon.jobs 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 below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at amazon.jobs . USA, TX, Austin - 132,100.00 - 178,800.00 USD annually USA, VA, Arlington - 132,100.00 - 178,800.00 USD annually USA, WA, Seattle - 132,100.00 - 178,800.00 USD annually

Requirements

Must-haves

Key job responsibilities
Own end-to-end delivery of data pipelines: design, build, test, deploy, and operate
Build and optimize ETL/ELT jobs that produce the foundational datasets powering the team's products and agentic workflows
Model structured and unstructured data (external insights, internal data, sales and delivery insights) so it is queryable and consumable by AI agents at scale
Design the data structures, schemas, and contracts that downstream services and agents depend on
Build and own data-quality tooling: validation, reconciliation, anomaly detection, and monitoring so bad data does not reach agents or models
Ship to production regularly in a high-ambiguity environment, balancing speed against reliability
Raise operational-excellence hygiene (data tests, monitoring, runbooks) for the pipelines you own
Write clear data models, pipeline design notes, and runbooks a peer can act on
About the team

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Application prep

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Keywords to mirror

awsaisql

Platform: amazon
Effort: medium

Benefits

Benefits detected in the listing.

Health Insurance

Dental

Vision

401(K)

Paid Time Off

Parental Leave