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.

BASIC QUALIFICATIONS

- 3+ years of data engineering experience

- 1+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience

- 1+ years of developing and operating large-scale data structures for business intelligence analytics using OLAP technologies experience

- 1+ years of developing and operating large-scale data structures for business intelligence analytics using data modeling experience

- 1+ years of developing and operating large-scale data structures for business intelligence analytics using SQL experience

- Experience with data modeling, warehousing and building ETL pipelines

- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions

PREFERRED QUALIFICATIONS

- Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)

- Experience in building analytic or scientific data products or solutions

- Experience managing confidential and sensitive employee information and adherence to strict confidentiality standards

- Experience with large scale distributed systems such as Hadoop, Spark etc.

- Experience using managed ML/AI solutions

- Experience with GenAI tools and techniques for data engineering automation

- Experience with monitoring and alerting frameworks for data pipeline reliability

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, WA, Seattle - 132,100.00 - 178,800.00 USD annually