The ideal candidate is passionate about understanding the business and making an impact with data. You ask sharp questions, design thoughtful data models, and build relevant pipelines with low tech debt. You know great data engineering isn't about shipping pipeline after pipeline — it's about data quality, governance, and knowing your sources well enough to make the right trade-offs. You'll build well-documented, context-rich datasets and self-service tools (new or reused) that put data directly in the hands of the people who need it most — applying data mining, data modeling, and AI to evaluate business health and drive measurable improvement..
Key job responsibilities
Know why you're building, not just what. Ask sharp questions first, understand your data and its significance, reuse before you rebuild, and set clear data contracts so data is always traceable to its source — and correct.
Own the design, development, and operation of scalable, production-grade, context-driven data pipelines — including the infrastructure behind them — keeping them lightweight, cost-efficient, auditable, and highly available to power metrics, reporting, analytics, and ML/LLM use cases.
Build context-rich "source of truth" datasets and reusable, dimensional data models that track the infrastructure delivery lifecycle (intake → deployment → decommission), serving as a trusted, intuitive-to-query foundation for downstream consumers while keeping tech debt in check.
Design data models and ETL/ELT workflows to ingest, transform, and integrate data from diverse sources using AWS services (e.g., S3, Redshift, Glue, EMR, Athena, Lambda) and internal Amazon data platform tools.
Define and champion data quality/ engineering and governance best practices — validation, monitoring/alerting, testing, design standards, documentation, and governance — to keep pipelines accurate, trustworthy, and well-maintained.
Partner with Software, Product, and other Data Engineering teams to source and model the right business entities, so any team can answer their own questions without waiting in a queue.
Troubleshoot operational and data-quality issues with root-cause analysis and durable fixes; audit existing pipelines, jobs, and queries, and recommend improvements to upstream sources for greater accuracy, simplicity, and scalability
About the team
We are the PAX Data and Analytics team — a small, high-conviction group of BI Engineers and Data Engineers embedded in one of the world's largest and fastest-growing data center organizations. We were established less than a year ago with a clear mandate: **turn operational complexity into decision-ready clarity.**
We don't build data pipelines and dashboards . We build *context* — the kind that lets leaders act with confidence and operators spot problems before they become incidents. Our work spans the full data lifecycle: sourcing and integrating raw operational signals, engineering relevant and reliable pipelines, and surfacing insights through self-service tools that hundreds of people rely on daily.
Every pipeline we ship, every standard we define, and every contract we establish becomes the foundation for what comes next
We are young, we are scrappy, and we are building something that will outlast us. If you want to lay foundations that an entire organization will stand on — you're in the right place.
4+ years of data engineering experience
Bachelor's degree or foreign equivalent in Computer Science, Engineering, Information Systems, Mathematics, or a related field
Experience with data modeling, warehousing and building ETL pipelines
Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
Experience with SQL
Experience working on and delivering end to end projects independently
Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)
Knowledge of batch and streaming data architectures like Kafka, Kinesis, Flink, Storm, Beam
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, WA, Seattle - 132,100.00 - 178,800.00 USD annually
