Sr. Data Engineer, Amazon Digital Advertising

Application deadline: Sep 21 2026

Are you excited by the idea of building the data foundation that an entire AI-powered product is built on from the very first pipeline Do you like the messy ambiguous problems: wrangling inconsistent data from dozens of outside partners and turning it into something clean timely and genuinely trustworthy If so wed love to talk.

Marketing at Amazon happens across a huge range of independent businesses and the data behind it is scattered across agencies vendors and platforms. Its slow to get inconsistent and almost impossible to see as a whole. Were a new AI-native team setting out to fix that building a service that automatically pulls all of this data together cleans and normalizes it and puts AI-powered analytics on top so anyone can ask a question in plain language and get a real answer.

Thats where you come in. As a Data Engineer on this team youll own the ingestion pipelines and the normalized dataset that everything else depends on. Youll figure out how to reliably pull data from noisy ever-changing external sources reconcile feeds that never quite agree with each other and build the quality checks that catch problems before anyone downstream ever sees them. AI is only as good as the data underneath it so your work sits squarely on the critical path if the data foundation is solid the whole product wins.

You wont just be maintaining pipelines. Youll help shape how data flows through a brand-new system and decide what good looks like for schemas and data quality. Its a small scrappy team with a lot of ownership to go around and youll get to leave your fingerprint on something from the ground floor.

If building trustworthy data infrastructure for hard never-been-done-before problems sounds like your kind of challenge wed love to hear from you.

Key job responsibilities
- Design build and operate scalable reliable data ingestion pipelines that collect marketing data from agencies ad tech vendors and internal sources on scheduled intervals without requiring source teams to change their current processes.
- Define and evolve input schemas and normalization logic that reconcile inconsistent granularity formats and terminology across sources into a unified dataset.
- Build automated data quality validation anomaly detection and reconciliation to catch and surface defects early reducing manual auditing and cleaning effort.
- Handle real-world data complexity such as delayed settlement of certain data sources mapping records to the correct hierarchies and deciding when to use planned versus finalized data.
- Partner with modeling and AI engineers to ensure the data foundation is structured for downstream analytics agent orchestration and natural-language query.
- Instrument pipelines for observability freshness/SLA monitoring and low-touch operations so the service requires primarily configuration updates as new sources are added.
- Collaborate with data providers and stakeholders to onboard new feeds and improve their timeliness completeness and accuracy.
- Contribute to a phased rollout starting with a core set of sources and expanding over time.

A day in the life
Your morning might start by reviewing pipeline health and resolving a data quality alert before anyone downstream is affected. Mid-morning you onboard a new external source reverse-engineering a messy partner feed and designing the schema to fold it cleanly into the shared dataset. After lunch you meet with a data provider to close gaps in their feed then review a teammates pull request. You close the day prototyping a better way to catch bad records at ingestion. Because this service is being built from scratch you operate with real autonomy and set the patterns others build on.

- 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

- 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 for more information. If the country/region youre applying in isnt 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 CO Boulder - 154600.00 - 209100.00 USD annually