If building trustworthy data infrastructure for hard, never-been-done-before problems sounds like your kind of challenge, we'd 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 teammate's 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 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, CO, Boulder - 154,600.00 - 209,100.00 USD annually
