Agentic Data Engineer, Frontier AI Tooling

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
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