Description

AWS Identity Analytics is reimagining how identity data is understood, acted on, and used to protect customers at scale. We build an AI-driven analytics platform that turns 50+ PB of raw logs and metrics into proactive, actionable insights for AWS Identity leadership and core service teams — including IAM and STS. AWS teams across the organization also rely on our platform for impact analysis related to AWS Auth.

Our platform is the foundation on which everything else stands: ingesting petabyte-scale data from dozens of Identity services, transforming it into structured, queryable intelligence, and serving it reliably to the ML models, LLM agents, and dashboards that our customers act on every day.

Are you excited by the prospect of building AI-powered solutions that let stakeholders access insights without needing to understand how the underlying data is organized or connected? Do you want to work on petabyte-scale data processing, enrichment, and querying engines? Do you want to work on a platform that directly shapes how AWS Identity services evolve — influencing decisions that affect hundreds of millions of customers globally? Do you thrive in ambiguous, fast-paced environments where your engineering work drives measurable business outcomes?

As a Software Development Engineer on the Identity Analytics team, you will own the data platform infrastructure that makes our AI and analytics capabilities possible. You will design and operate the ingestion, transformation, and serving pipelines that feed our ML models and LLM-powered agents. You will be the engineering partner to our Applied Scientist — translating research prototypes into production-grade systems that run reliably at scale. What makes this role distinct is the combination of deep platform engineering with direct scientific impact: the pipelines you build and the infrastructure you operate determine the quality, freshness, and reliability of every insight our customers receive.



Key job responsibilities
• Design, build, and operate scalable data ingestion, transformation, and loading pipelines that process petabyte-scale Identity logs, metrics, and policy data from IAM, STS, and other AWS Identity services — using services such as AWS Glue, EMR, Spark, Athena, S3, and Redshift.
• Own the productionization lifecycle for ML models developed by the Applied Scientist: package, deploy, monitor, and maintain models in production environments using SageMaker, ECS, and EKS — ensuring reliability, latency, and scalability meet production standards.
• Build and maintain the feature engineering infrastructure that transforms raw Identity data into structured datasets ready for ML training, evaluation, and inference.
• Drive platform resilience and operational excellence — designing for failure, building robust monitoring and alerting, reducing operational load through automation, and ensuring the platform scales automatically to the demands of incoming data.
• Partner with the Applied Scientist, BIEs, and product managers to understand analytical requirements, design data models that support both current and future use cases, and ensure the platform evolves ahead of customer needs.
• Identify and build onboarding capabilities that reduce the time it takes for new Identity service teams to integrate their data into the platform and begin consuming insights.
Contribute to the team's technical direction by participating in design reviews, raising the engineering bar through code reviews, and bringing a systems-thinking perspective to how the platform scales over the next three to five years.