Amazon is seeking a Business Intelligence Engineer (BIE) to support Vendor Investigation and Transaction Accuracy (VITA). VITA’s mission is to detect and prevent theft, fraud, abuse and waste globally. We use advance techniques to prevent erroneous payments and to protect Amazon shareholders. We are seeking a BIE to support our automation and data needs. The ideal candidate thrives in a fast-paced environment, relishes working with large transactional volumes and big datasets, and is passionate about data and analytics. As a BIE, the candidate must deliver robust, structured reporting and should be proficient with SQL/data warehousing solutions and work independently on reporting challenges. The BIE should have Accounts payables domain knowledge and must be eager to dive deep into invoice, vendor, and payment data to develop fraud, risk, and erroneous payment analytics. The BIE will identify manual investigation opportunities and develop visualization dashboards and a robust metric reporting infrastructure to be used by VITA investigators and senior leadership.
The ideal candidate will thrive on using statistical analysis to address VITA’s biggest problems in identifying defects. The candidate will have opportunities to (and will be asked to) get exposure to the modern cloud-based data technologies. This individual must be proactive in taking ownership of data ingestion from variety of source systems, create data pipelines and analytics, have excellent problem-solving abilities and, have deep knowledge of business intelligence solutions. The individual must have working knowledge of database optimization, data modelling techniques and build data warehouse and reporting solutions. The individual must have the ability to mentor other BIEs, work with technology, product development, finance, and business teams. The ideal candidate must have excellent communication, organizational, and prioritization skills with the ability to handle multiple tasks simultaneously.
Key job responsibilities
• Translate business risks and needs into the development of analytics for fraud and risk detection
• Coordinate with technical teams as appropriate to develop and implement analytics and reporting needs
• Partner with stakeholders to gather requirements and integrate necessary data sources to support business analysis and reporting
• Design and implement data warehouse / business intelligence and analytics solutions. Take ownership of critical reports and dashboards
• Design optimized data model and create data ingestion and data loading pipelines
• Implement end to end optimized solutions and recommend design improvements via re-architecture or automated solutions
• Recognize and adopt best practices for building a scalable business intelligence platform
• Apply analytics and data mining techniques to solve complex problems, drive business decisions and identify major gaps and opportunities
• Assess trends, analyzes data and proposes hypotheses to identify potential fraud patterns and financial abuse
• Collaborates with the business to research, develop and test new fraud detection and prevention rules
• Coordinate with global team members to conduct deep dives walk-throughs and quality reviews of evidence to resolve complex problems.
• Mentor and provide technical guidance to other BIEs on resolving data quality issues. Act as Subject Matter Expert when it comes to data questions.
- 5+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience with data modeling, warehousing and building ETL pipelines
- Experience writing complex SQL queries
- Experience developing and presenting recommendations of new metrics allowing better understanding of the performance of the business
- Knowledge of SQL and data warehousing concepts
- Bachelor's degree in sciences, engineering, finance or equivalent
- 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
- Experience with forecasting and statistical analysis