AmazonBI Engineer I, AWS DC Global Network Delivery
Amazon is hiring a BI Engineer I, AWS DC Global Network Delivery in Seattle, United States. It pays $82k-$144k a year and Level rates it ; you can apply on Level.
AI in this role
Build datasets, metrics, and dashboards to track AWS global network infrastructure delivery performance.
You'll join a diverse, cross-functional team of Product managers, TIPMs, network engineers, planners, builders, and operations leaders around the world. Together we work across AWS to deliver the highest standards of safety and security while providing seemingly infinite capacity at the lowest possible cost. Our culture is inclusive, welcomes new ideas, and gives you room to own your work from start to finish.
We're looking for a Business Intelligence Engineer who is curious, learns quickly, and wants to grow by working on real, high-impact problems. You'll build the datasets, metrics, and dashboards that show how infrastructure delivery is performing, from intake through deployment to decommission. You'll work alongside senior BI Engineers, Data Engineers, and business partners to turn business questions into reliable, well-documented data.
What makes this role exciting? Your work won't sit unused. The datasets and dashboards you build will be used every week by planners, operators, and leaders to track delivery milestones and decide what to do next. You'll learn how a global infrastructure business runs, get hands-on with AWS data tools and AI-assisted analytics, and grow your skills through code reviews and mentorship from experienced engineers.
Key job responsibilities
- Work with business stakeholders, TIPMs, and senior team members to gather requirements for well-defined reporting and analysis needs, then translate them into queries, data models, and dashboards.
- Build and maintain datasets, ETL pipelines, and dashboards using SQL, Python, and AWS tools such as Redshift, S3, and QuickSight
- Follow team standards for code reviews, data quality checks, documentation, and monitoring so your work is reliable and reusable
- Run pipeline health checks, investigate data issues, and find the root cause, escalating to the right owners when needed
- Analyze data to answer business questions and clearly explain what you found, including caveats, to stakeholders and your team
- Use metric definitions and the team's data contracts consistently, and point out gaps or inconsistencies you find in source data
- Try out AI tools and new approaches that make analysis faster or more insightful
- Keep learning from code reviews, feedback, and senior teammates, and share what you learn with the team.
A day in the life
A day in the life Your morning starts with dashboard pipeline health checks and a look at new intake requests. Then you dig into your current project: learning the source data, asking what decision a metric will support, and confirming requirements with your stakeholder before you write code. In the afternoon, you might build or update a dataset, refactor part of a legacy pipeline with guidance from a senior engineer, or walk a stakeholder through a draft dashboard. You finish the day by documenting what you learned and submitting your code for review. You'll be expected to understand a problem before building, and to be open about what you're unsure of.
About the team
We're the PAX Data and Analytics team, a small group of BI Engineers and Data Engineers inside one of the world's largest and fastest-growing data center organizations. We started less than a year ago with one mandate: turn operational complexity into decision-ready clarity.
We do more than build dashboards. We build context that helps leaders act with confidence and helps operators catch problems before they become incidents. Our work covers the full data lifecycle: sourcing raw operational data, building reliable pipelines, and delivering self-service tools that hundreds of people use every day. Every pipeline we ship and every standard we define becomes the foundation for what comes next. We're young, we're scrappy, and we're building something that will outlast us. It's a great place to learn and grow your career.
Basic qualifications
- 2+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience with one or more industry analytics visualization tools (e.g. Excel, Tableau, QuickSight, MicroStrategy, PowerBI) and statistical methods (e.g. t-test, Chi-squared)
- Experience with scripting language (e.g., Python, Java, or R)
Preferred qualifications
- Master's degree, or Advanced technical degree
- Knowledge of data modeling and data pipeline design
- Experience with statistical analysis, co-relation analysis
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 - 82,300.00 - 144,000.00 USD annually
How we rate this
BI Engineer I, AWS DC Global Network Delivery at Amazon rates 25 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.
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. Open the description and count.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where etl was part of your work. What did you do?
- Tell me about a project where data modeling was part of your work. What did you do?
- Tell me about a project where dashboards was part of your work. What did you do?
- Tell me about a project where data analysis was part of your work. What did you do?
- Walk me through how you've used SQL in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: ETL, Data Modeling, Dashboards, Data Analysis, and SQL. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
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