Business Intelligence Engineer, Internal Audit - Data Analytics Support

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.