Business Intelligence Engineer, Internal Audit

- 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)
- Master's degree, or Advanced technical degree
- Knowledge of data modeling and data pipeline design
- Experience with statistical analysis, co-relation analysis
- 1+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience

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, MA, Boston - 82,300.00 - 144,000.00 USD annually
USA, NJ, Jersey City - 90,500.00 - 158,400.00 USD annually
USA, VA, Arlington - 82,300.00 - 144,000.00 USD annually

Internal Audits mission is to help our businesses improve controllership, operational efficiency, and customer experience. Amazon is seeking a Business Intelligence Engineer for our Internal Audit Data Analytics team that provides data analytics services and solutions to enable our audit programs to scale with Amazons growth and complexity. This is a highly visible opportunity to provide custom-build solutions and services that will increase the productivity of Amazons audit function using data.

As a Business Intelligence Engineer, you will work on audit engagements to help with data analytics in various audit phases, provide training to auditors on data analytics tools and technology, set up data infrastructure for working with data, and create dashboards. You will work closely with Amazon businesses and functions to assess audit requirements for data analytics. You will have a relentless focus on the customers and dive deep into the challenges they face. You will also leverage machine learning and statistical techniques for advanced use cases.

Key job responsibilities
• Operate in a consultative capacity, analyze the functions, objectives and requirements of organizations within the company, and translate business requirements into technical requirements.
• Identify and substantiate risks associated with business processes, policy, organization, procedures, and systems using data analysis and recommend remediation steps that reinforce business assurance.
• Seek out the correct data sources required and using ETL (Extract, Transform, and Load) methods to join, manipulate, and normalize the data into a consumable data set for testing and analysis.
• Determine the appropriate method of data collection and research methodology, analyze and interpret results into actionable testing solutions which are summarized for audit findings.
• Develop analyses & tools that will enable deeper analysis while increasing efficiency and effectiveness by reducing manual intervention while increasing sample sizes tested.