Manager, Business Intelligence, Fraud Investigations, Recovery & Enforcement (FIRE)

Are you a data leader who wants to reinvent business intelligence around AI, and put that reinvention to work protecting Amazon from fraud and financial loss? Do you want to lead a team that is moving beyond static dashboards to production-grade AI agents that investigate, detect, and act autonomously?

The Fraud Investigations, Recovery and Enforcement (FIRE) team is looking for a Manager, Business Intelligence to lead a team of Business Intelligence Engineers and Data Engineering that owns fraud prevention, detection, and investigation analytics for FinOps. This is not a traditional reporting role. We are transforming BI from a passive, dashboard-centric function into an AI-powered fraud analytics engine, embedding AI in every layer of the stack, and shifting our stakeholders from reading static reports to interacting with active, conversational, and autonomous workflows that surface risk, explain it, and act on it.

You will set the technical and analytical direction for a team that has already delivered multi-agent investigation platforms, agents that cut per-query handling time by roughly 70%, and a billion-row entity layer that serves as a single source of truth across FIRE's fraud programs. Your mandate is to push this further: make AI the default in reporting, detection, and investigation; convert manual, human-in-the-loop analysis into agentic and self-service workflows; and keep the underlying data platform robust and scalable enough to deploy AI models with confidence.

This is a hands-on leadership role. You will coach BIEs and DE toward high-judgment ownership, partner deeply with Data Science, FinTech, and FinAuto, and work directly with FIRE Investigators and Program Managers to translate ambiguous fraud problems into agentic, measurable solutions. The position is based in Hyderabad and reports to FIRE leadership. The ideal candidate is a Think Big, invent-and-simplify leader who is energized by building AI at the frontier of fraud analytics and by growing engineers who operate above their level.

Key job responsibilities - Lead, hire, develop, and retain a team of 4 Business Intelligence Engineers and 1 Data Engineer; set a high bar, create structured growth paths, and grow engineers who consistently operate above level. - Own the BI strategy and roadmap for FinOps fraud prevention, detection, and investigation, spanning reporting, analytics, detection models, data platform, and automation. - Embed AI in every layer of business intelligence, from data ingestion and entity resolution to detection, investigation, and reporting, and set the standard for how the team designs, evaluates, and productionizes AI/LLM solutions. - Drive the shift from passive, static dashboards to active, conversational, and autonomous workflows, including agentic investigation tools, conversational and self-service data access, and autonomous detection and dispositioning that reduce human effort and time-to-action. - Partner with FIRE Investigators and Program Managers to translate fraud and investigation problems into agent architectures and analytical solutions, securing stakeholder alignment across fraud program leadership. - Own and evolve FIRE's fraud data platform (for example, entity and risk-intelligence layers) so it is robust, scalable, and reliable enough to serve production AI models and self-service prototyping at billion-row scale. - Partner with Data Science, FinTech, and FinAuto to establish and streamline the prototype-to-production lifecycle, reduce deployment overhead, and enable direct collaboration across environments. - Define and own the metrics and savings methodology that quantify fraud rates, exposure, loss, recovery, and detection coverage, and automate P0, WBR, and MBR reporting via agents. - Establish and enforce best practices in data integrity, model and change management, code quality, testing, and documentation across the team. - Prioritize competing requests across fraud programs and investigations, balancing urgent operational needs with durable platform and AI investments. - Communicate architecture decisions, trade-offs, insights, and recommendations clearly to technical and non-technical stakeholders, including senior leadership, across regions.

A day in the life You start by reviewing the health of the fraud data platform and the agents running in production, checking that overnight incremental refreshes landed and that the investigation and reporting agents are performing within expectations. You meet 1:1 with a BIE architecting a new detection agent, coaching them on the agent design, evaluation strategy, and how to secure alignment from Retail Fraud leadership. You then work with a Program Manager and a Data Scientist to scope a conversational, self-service workflow that lets investigators query case data in natural language instead of waiting on a dashboard. In the afternoon, you review a Data Engineer's entity-layer pipeline design to ensure it scales for the next set of fraud entities, align with FinAuto and FinTech on the prototype-to-production path, and present detection-coverage and savings-methodology findings to senior stakeholders. Throughout, you balance shipping today's automation against the platform and AI investments that make FIRE's analytics increasingly autonomous.

Basic Qualifications: - 7+ years of business intelligence and analytics experience - 5+ years of delivering results managing a business intelligence or analytics team, including employee development and performance management experience - Experience with SQL - Experience with ETL - Experience with data visualization using Tableau, Quicksight, or similar tools - Experience with R, Python, Weka, SAS, Matlab or other statistical/machine learning software

Preferred Qualifications: - 4+ years of working with very large data warehousing environment experience - 10+ years of data warehouse technical architectures, data modeling, infrastructure components, ETL/ ELT and reporting/analytic tools and environments, data structures and hands-on SQL coding experience - Experience across the domain of risk management & fraud - Experience with various statistical techniques (regression analysis, coefficient correlation) - Experience with Generative AI architecture patterns, including multi-agent orchestration, RAG, and fine-tuning, and building agentic, conversational, or autonomous workflows.

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