Data Engineer II, IAM and Abuse Prevention

About the role

The ISAP SafeGuard team mixes long-term, high-impact projects with near-term innovative solutions to prevent abuse across Amazon. We balance Bias for Action, Dive Deep, Invent and Simplify, and Customer Trust daily. Our team embraces new approaches, technology, and innovation while ensuring our solutions are scalable, accurate, and drive action. We work with some of the most sensitive data at Amazon, which requires thoughtful engineering, strict access controls, and a strong sense of responsibility. If you are energized by building data systems and solutions that directly protect customers and sellers from bad actors, at scale, this is the team for you. Diverse Experiences Amazon Security values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying. Why Amazon Security? At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon's products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety o

Requirements

• collaborate with data scientists and applied scientists to support ai and ai-agent-centric solutions, ensuring proper data engineering underpins model training and inference
• leverage genai and ml tools to enhance your own workflows, automate pipeline operations, and improve data quality processes
• develop deep understanding of partner teams and their capabilities, identifying opportunities to consume new signals and vend intelligence data to downstream consumers

Additional Information

Amazon's Identity Security & Abuse Prevention (ISAP) team is seeking a Data Engineer to join our team. We discover, analyze, and quantify security risks across Amazon's identity and authentication landscape, transforming complex behavioral patterns into actionable intelligence that empowers teams to proactively defend against abuse and unauthorized access. In this role, you will design, build, and own scalable data pipelines and infrastructure that power security investigations, abuse detection, and intelligence products across multiple Amazon verticals. You will work with large, sensitive datasets spanning security telemetry, authorization logs, customer signals, and device metadata to create the data foundation that enables our security engineers, data scientists, and applied scientists to identify and prevent abuse at scale. This is a high-impact role where your work directly protects Amazon customers and sellers. You will own both existing infrastructure (reviewing and modernizing current pipelines) and greenfield builds (designing and launching data systems for new security products and investigation capabilities). You will partner with science teams to engineer ML-ready datasets and work at the intersection of traditional data engineering and AI-driven solutions. Key job responsibilities - Design, implement, and maintain scalable ETL/ELT pipelines that ingest and transform security telemetry, authorization logs, compliance data, and operational metrics from diverse sources across Amazon - Build and optimize data models that enable security engineers and scientists to efficiently query and analyze abuse patterns across billions of events - Create ML-ready datasets for data science and applied science teams - Own monitoring, alerting, and observability for all data pipelines and data solutions, proactively identifying and resolving data quality issues - Review and modernize existing data infrastructure, proposing architectural improvements that increase reliability, reduce cost, and improve performance - Design and build new data capabilities from the ground up to support product launches and investigation team needs - Partner with security engineers, scientists, and investigators to understand their data requirements and build solutions that accelerate abuse detection and response - Collaborate with data scientists and applied scientists to support AI and AI-agent-centric solutions, ensuring proper data engineering underpins model training and inference - Leverage GenAI and ML tools to enhance your own workflows, automate pipeline operations, and improve data quality processes - Develop deep understanding of partner teams and their capabilities, identifying opportunities to consume new signals and vend intelligence data to downstream consumers A day in the life You might start your morning reviewing pipeline health dashboards, triaging an alert on a data freshness SLA, and pushing a fix before downstream consumers are impacted. Mid-morning, you join a design review with the applied science team on a new abuse detection model, working through the data schema and feature engineering requirements together. After lunch, you prototype a new ingestion path for a customer signal dataset that an investigator identified as high-value for a current case. Later, you pair with a security engineer to optimize a query that powers a leadership dashboard. You close the day by documenting a proposed architecture for a new data product that will correlate device metadata with authentication anomalies, preparing it for team review.

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