AmazonPosted 2d ago

Data Engineer III, AWS Marketplace Demand Generation & Lifecycle Engagement

Data Engineer III, AWS Marketplace Demand Generation & Lifecycle Engagement at Amazon scores 65 out of 100 on AI centrality, which makes it a Level 3 role on this board.

US, WA, Seattleseniorfull-time$155k-$209k

AI in this role

First dedicated data engineer on a data science team, building and operating production pipelines for ML scoring engines and marketing intelligence systems.

ml-scoring-enginesauto-nurture-pipelinesattribution-models

data-engineeringmlopsfeature-engineeringdata-architecturepipeline-development

This is a foundational hire — you will be the first dedicated data engineer on a data science team embedded within the B2B Marketing organization of AWS Marketplace. You will own the data infrastructure that powers marketing intelligence systems: ML scoring engines that decide which customers to reach, auto-nurture pipelines that personalize outreach at scale, attribution models that connect marketing activity to revenue, and customer economics frameworks that predict lifetime value. The groundwork has been laid over two years with production ML models and live pipelines already in place. What comes next is yours to define — there is significant room to shape the architecture, set standards, and make decisions that determine how data infrastructure evolves on this team.

Key job responsibilities
- Design, build, and operate production data pipelines that serve ML models and analytics — including feature engineering, model scoring infrastructure, output delivery, and data quality enforcement.
- Define and own the team's data architecture, making trade-offs between speed and durability, cost and scalability, and short-term delivery versus long-term maintainability.
- Build and maintain analytical datasets that stitch together product activity, marketing engagement, customer transactions, and revenue data into query-ready models that enable analysis not previously possible.
- Produce well-documented, maintainable code built for others to extend — establish patterns and templates that raise the quality bar for all data work on the team.
- Partner with data scientists, marketers, and cross-functional engineering teams to gather requirements, define data contracts, influence upstream data producers, and ensure downstream consumers can self-serve.

A day in the life
Your scope spans the full ML lifecycle: ingestion from multiple data platforms, feature engineering for model training, production pipeline deployment, model output delivery, and the reporting layer that makes it all consumable. Some problems are well-scoped and need execution; others are ambiguous and need you to define the path. You will wear multiple hats — data modeling, pipeline engineering, infrastructure automation, and partnering directly with data scientists on systems that are genuinely new.

About the team
We are a data science team within the B2B Marketing organization of AWS Marketplace — AWS's digital catalog where customers discover, evaluate, and purchase third-party software. We build measurement, targeting, and intelligence systems that power marketing decisions: multi-touch attribution models, ML-powered targeting engines, customer unit economics frameworks, experimentation platforms, and partner intelligence systems. Our aspiration is to build an end-to-end intelligent B2B marketing platform where data science and AI are at the core of every decision. Because you will work shoulder-to-shoulder with data scientists building production AI systems, this role is a natural path to deepen your expertise in data science and machine learning — understanding not just how to move data, but how models consume it and how engineering decisions shape AI outcomes. If you want to help build something at the frontier of AI-powered B2B marketing alongside a collaborative, technically rigorous team, this is where to do it.

Basic qualifications

- 5+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with SQL
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
- Experience mentoring team members on best practices

Preferred qualifications

- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience operating large data warehouses

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 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 https://amazon.jobs/en/benefits.

USA, MA, Boston - 154,600.00 - 209,100.00 USD annually
USA, VA, Herndon - 154,600.00 - 209,100.00 USD annually

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Skills and AI tools this role asks for

Data EngineeringMlopsFeature EngineeringData ArchitecturePipeline DevelopmentMl Scoring EnginesAuto Nurture PipelinesAttribution Models

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