Business Intelligence Engineer

- 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
- 1+ years of processing large, multi-dimensional datasets from multiple sources experience
- 1+ years of performing statistical analysis experience
- 1+ years of developing automated reporting experience
- 3+ years of in the job offered or a related occupation experience
- 1+ years of using SQL, ETL (Extract, Transform, Load), or Oracle experience
- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, Statistics, Economics, or a related field
- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience with data modeling, warehousing and building ETL pipelines
- Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling
- Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift
- Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets

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, NY, New York - 109,500.00 - 185,000.00 USD annually
USA, WA, SEATTLE - 99,500.00 - 160,000.00 USD annually
Amazon Advertising is one of the fastest-growing businesses at Amazon, helping advertisers of all sizes reach and engage customers across our properties. We are looking for a Business Intelligence Engineer to join the analytics team supporting our Advertiser Experience organization.

Our team is committed to delivering insights on the advertiser experience within the advertising console. We're looking for a teammate who can deep dive into multiple areas of the business, generate dependable Weekly Business Review (WBR) reporting, and build dashboards that power regular deep dives and business reviews. Just as important, we need someone who thrives in a fast-changing environment, especially as it relates to AI. Our team is invested in using AI to accelerate our speed to insight, and we're looking for someone who consistently seeks out opportunities to improve productivity, time to insight, and scalability across the org.

If you're passionate about turning ambiguous business questions into clear, trustworthy analytics, and excited to shape how a team adopts AI to work faster, we'd love to hear from you.

Key job responsibilities
1. Build and maintain dependable WBR reports and self-service dashboards that leadership and partner teams rely on for regular reviews and deep dives.
2. Deep dive into multiple areas of the advertiser experience to surface actionable insights, identify trends, and answer complex business questions.
3. Design, develop, and optimize data pipelines, data models, and datasets that are accurate, scalable, and easy to consume.
4. Partner with product managers, analysts, and business stakeholders to define metrics, translate requirements into analytical solutions, and drive decisions with data.
5. Identify and implement opportunities to apply AI and automation to reduce time to insight and improve team productivity and scale.
6. Establish and uphold data quality, documentation, and reporting standards so insights are trustworthy and repeatable.

A day in the life
You might start your day validating the numbers behind this week's WBR, then jump into a deep dive on a shift in an advertiser experience metric that leadership flagged. In the afternoon, you could be building a new dashboard for an upcoming business review, pairing with an analyst to define a new metric, or prototyping an AI-assisted workflow that cuts a recurring analysis from hours to minutes. You'll balance recurring reporting commitments with exploratory analysis, always looking for ways to make the team faster and more scalable.