Job description

Amazon Advertising's Self-Service Support team ensures that advertisers using our applications can answer their own questions or solve their own problems whenever possible through Help content — and have an easy path to contact Amazon when it's not. We're looking for a curious and resourceful Data Engineer to own our infrastructure, deploy production models, fine-tune our data pipelines, and drive our agentic AI initiatives. Key job responsibilities In this role, you'll be the engineering backbone of our team — owning the systems that power intelligent, self-service experiences for advertisers at scale. You'll deploy ML models into production, optimize data pipelines for performance and reliability, and lead the development of agentic workflows that autonomously reason and act on behalf of our customers. ## Key Responsibilities - **Infrastructure Ownership** — Design, build, and maintain scalable data infrastructure (data lakes, warehouses, streaming systems) that supports our analytics and ML workloads - **Production Model Deployment** — Own the end-to-end lifecycle of deploying ML models into production, including CI/CD pipelines, model serving, monitoring, and rollback strategies - **Pipeline Engineering & Optimization** — Develop and fine-tune ETL/ELT pipelines for performance, reliability, and cost efficiency; instrument for observability and SLA adherence - **Agentic AI Development** — Drive the design and implementation of agentic workflows — autonomous systems that reason, plan, and take actions to improve advertiser self-service experiences - **Reliability & Scalability** — Ensure production systems meet uptime, latency, and throughput targets; lead incident response and root-cause analysis - **Collaboration** — Partner with Data Scientists, Applied Scientists, Product, and Content teams to translate research into production-ready solutions that help advertisers help themselves - 1+ years of data engineering experience - Experience with data modeling, warehousing and building ETL pipelines - Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala) - Experience with one or more scripting language (e.g., Python, KornShell) - Experience with big data technologies such as: Hadoop, Hive, Spark, EMR - Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc. 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, NY, New York - 111,400.00 - 185,000.00 USD annually USA, WA, SEATTLE - 101,300.00 - 160,000.00 USD annually

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Key job responsibilities
## Key Responsibilities
Infrastructure Ownership** — Design, build, and maintain scalable data infrastructure (data lakes, warehouses, streaming systems) that supports our analytics and ML workloads
Production Model Deployment** — Own the end-to-end lifecycle of deploying ML models into production, including CI/CD pipelines, model serving, monitoring, and rollback strategies
Pipeline Engineering & Optimization** — Develop and fine-tune ETL/ELT pipelines for performance, reliability, and cost efficiency; instrument for observability and SLA adherence
Agentic AI Development** — Drive the design and implementation of agentic workflows — autonomous systems that reason, plan, and take actions to improve advertiser self-service experiences
Reliability & Scalability** — Ensure production systems meet uptime, latency, and throughput targets; lead incident response and root-cause analysis
Collaboration** — Partner with Data Scientists, Applied Scientists, Product, and Content teams to translate research into production-ready solutions that help advertisers help themselves
1+ years of data engineering experience
Experience with data modeling, warehousing and building ETL pipelines
Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
Experience with one or more scripting language (e.g., Python, KornShell)
Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
USA, NY, New York - 111,400.00 - 185,000.00 USD annually
USA, WA, SEATTLE - 101,300.00 - 160,000.00 USD annually

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Keywords to mirror

aimlci/cdsqlpython

Platform: amazon
Effort: medium

Benefits

Benefits detected in the listing.

Health Insurance

Dental

Vision

401(K)

Paid Time Off

Parental Leave