AmazonSr Business Intelligence Engineer, Amazon Global Data Center Ops Central Insight and Analytics Team
Amazon is hiring a Sr Business Intelligence Engineer, Amazon Global Data Center Ops Central Insight and Analytics Team in Seattle, United States. It pays $130k-$176k a year and Level rates it ; you can apply on Amazon.
Level's read on this job
- Level rates this role Little AI ●○○○: aI is not part of the work.
- Tools and skills the posting cites include SQL, Python and R.
- Level classifies it as a senior role.
- Level lists 20,000+ other open roles at Amazon.
AI in this role
Build diagnostic analytics and decomposition frameworks for Amazon's global data center operations.
GDCO operates one of the world's largest physical infrastructure fleets — hundreds of data centers across 20+ countries — with thousands of technicians performing hardware repairs, rack installations, and preventive maintenance daily. We have strong descriptive analytics (dashboards, WBR metrics), but has opportunity in terms of explaining root causes, attribute performance gaps to specific factors, or recommend proven corrective actions. This leader will focus on building that diagnostic layer.
This is a high-impact, high-autonomy role. You will scope analytical problems, build decomposition frameworks, partner with operational leaders to validate findings, and deliver insights that directly influence resource allocation, process design, and investment decisions at the VP level. You'll work with rich operational data at scale: millions of repair tickets, rack lifecycle events, parts inventory flows, workforce scheduling data, and hardware validation results.
Basic qualifications
- 10+ years of performing statistical analysis experience
- Expert SQL skills — complex analytical queries across large-scale datasets (multi-system joins, window functions, statistical aggregations across petabyte-scale data)
- Strong statistical foundation — regression analysis, statistical process control, hypothesis testing, and metric decomposition applied to real business problems
- Experience building automated, reproducible analytical pipelines — scheduled systems that serve ongoing business processes at production quality
- Proficiency in Python or R for data manipulation, statistical analysis, and visualization
- Demonstrated ability to decompose complex business metrics into attributable components — translating "this metric moved" into "here's why, here's who owns each piece, here's the impact"
- Strong written and verbal communication — ability to write diagnostic narratives and present complex analysis clearly to VP-level audiences
Preferred qualifications
- Experience working directly with business stakeholders to translate between data and business needs
- Experience in operational analytics — manufacturing, logistics, field operations, supply chain, or physical infrastructure domains where you've analyzed process efficiency, failure modes, or workforce productivity
- Experience with workforce analytics — productivity measurement, skill-gap analysis, labor planning, or efficiency modeling that accounts for varying task complexity and work mix
- Experience building composite metrics or indices that combine multiple dimensions into weighted scores (similar to OEE, NPS, or operational health indices)
- Experience with multivariate analysis — identifying which factors among many are most strongly associated with performance outcomes
- Experience building decision-support tools, recommendation frameworks, or analytical playbooks — structured systems where analysis directly translates to operational action
- Familiarity with forecasting methods (time-series decomposition, trend analysis, seasonality modeling) for operational planning
- Experience building analytical frameworks adopted across multiple teams — reusable tools and methods that scale beyond individual analyses
- Experience with Amazon internal data tools (Redshift, Athena, QuickSight) is a plus for internal candidates
- Experience with data center, cloud infrastructure, or hardware operations is valuable but not required — domain knowledge can be learned; analytical rigor cannot
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 - 130,400.00 - 176,300.00 USD annually
How we rate this
Sr Business Intelligence Engineer, Amazon Global Data Center Ops Central Insight and Analytics Team at Amazon rates 0 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. Read the description and count the AI tools, models and workflows it names.
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 business intelligence was part of your work. What did you do?
- Tell me about a project where statistical analysis 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?
- Tell me about a project where metric decomposition 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: Business Intelligence, Statistical Analysis, Data Analysis, Metric Decomposition, 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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