Data Engineer II, Eva Trust Dev Team

Amazon is hiring a Data Engineer II, Eva Trust Dev Team in Bellevue, United States. It pays $132k-$179k a year and Level rates it ; you can apply on Amazon.

Level's read on this job

On Level's scale, this data engineer role is Little AI ●○○○: aI is not part of the work.
Level classifies it as a mid-level role.
Level lists 20,000+ other open roles at Amazon.
The role was posted in the last few days.

AI in this role

AWS Infrastructure Services owns the design, planning, delivery, and operation of all AWS global infrastructure. In other words, we’re the people who keep the cloud running. We support all AWS data centers and all of the servers, storage, networking, power, and cooling equipment that ensure our customers have continual access to the innovation they rely on. We work on the most challenging problems, with thousands of variables impacting the supply chain — and we’re looking for talented people who want to help.

You’ll join a diverse team of software, hardware, and network engineers, supply chain specialists, security experts, operations managers, and other vital roles. You’ll collaborate with people across AWS to help us deliver the highest standards for safety and security while providing seemingly infinite capacity at the lowest possible cost for our customers. And you’ll experience an inclusive culture that welcomes bold ideas and empowers you to own them to completion.

Own the data pipelines that let 30K+ AWS data center engineers get trusted, data-backed answers from Eva, our generative AI assistant. You'll lead the move of operational data from manual processes to automated pipelines, dashboards that generate themselves, and knowledge Eva can use the moment someone needs it.

As a Data Engineer II on the Eva Trust team, you'll design, build, and operate the pipelines behind data center analytics. You'll own data domains end to end: you'll ingest data from operational systems, transform and validate it, and publish trusted datasets. Those datasets feed automated dashboards and structured knowledge bases, so insights reach people directly in Eva instead of through manually maintained reports.

Key job responsibilities
- Design, build, and own data pipelines that ingest, transform, and validate data center operational data from multiple source systems
- Lead the migration of manual, high-maintenance data processes and legacy dashboards to automated, monitored pipelines
- Design data models and publish trusted, reusable datasets that power automatically generated dashboards
- Turn curated data into structured knowledge bases and Eva skills so insights show up directly in Eva
- Set and enforce data quality, freshness, and monitoring standards, including alarms and runbooks
- Find and retire brittle or duplicate data dependencies, and improve pipeline reliability and cost
- Work with data center partners to understand their data sources and needs, then turn ambiguous asks into clear plans
- Write clean, tested, documented code, take part in design and code reviews, and mentor junior engineers and interns

A day in the life
You might start the day reviewing pipeline health and deciding how to fix a recurring freshness issue at its source. Then you meet a data center operations partner to plan how a widely used dashboard's data moves into Eva. In the afternoon you write the design for a new ingestion pipeline, review a junior engineer's code, and work with the Eva platform team on how a new knowledge base plugs in. You finish by checking Eva's answers against your published data.

About the team
Eva Trust is part of the Data Center Gen AI team in AWS Infrastructure Services. We make sure Eva, the AI assistant used by 30K+ people across the data center community, is accurate, safe, and trusted, and that it brings up the right information where people need it. We own the data pipelines, dashboards, structured knowledge, evaluations, and guardrails behind Eva. We combine deep data center domain expertise with data and AI engineering, and we invest in growing the people around us.

*Why AWS*
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.

*Diverse Experiences*
Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.

*Work/Life Balance*
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.

*Inclusive Team Culture*
Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness.

*Mentorship and Career Growth*
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

Basic qualifications

- 3+ years of data engineering experience
- 1+ years of developing and operating large-scale data structures for business intelligence analytics using data modeling experience
- 1+ years of developing and operating large-scale data structures for business intelligence analytics using SQL experience
- 1+ years of developing and operating large-scale data structures for business intelligence analytics using each of the following: ETL (Extract, Transform, Load)/ELT (Extract, Load, Transform) processes experience
- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Information Systems, Mathematics, or a related field

Preferred qualifications

- 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)

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, Bellevue - 132,100.00 - 178,800.00 USD annually

How we rate this

Data Engineer II, Eva Trust Dev Team at Amazon rates 26 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.