Software Development Engineer, AWS Payments & Fraud Prevention

What if the data you engineer could stop fraud before it happens - and protect millions of AWS customers worldwide?
The Payments and Fraud Data Platform team within AWS Payments & Fraud is the backbone of one of the most critical missions at AWS: ensuring every transaction is safe, every suspension is justified, and every customer experience is seamless. We process petabytes of data daily, powering the ML models, rule engines, and real-time reporting systems that allow us to detect bad actors.
We are looking for a Software Development Engineer who is passionate about building large-scale data systems, thrives in ambiguity, and wants to work at the intersection of big data, machine learning, non-payment and fraud prevention.

Key job responsibilities
Architect & Build Petabyte-Scale Data Pipelines - Design, develop, and optimize batch and near-real-time data pipelines using Amazon Managed Airflow (MWAA), Fargate, EMR, Apache Iceberg, and Amazon Redshift to ingest, transform, and serve data at massive scale.
Power ML & Rules Engines - Deliver high-quality, timely datasets that fuel non payment and fraud detection ML models and business rule engines, directly impacting fraud prevention and non-payment suspension accuracy.
Build Intelligent Data Access Layers - Design and implement a Smart Query & Data Access Analyzer that profiles query patterns, identifies hot data, and automatically promotes frequently accessed datasets into hot memory tiers (e.g., OpenSearch, DynamoDB DAX, Redshift RA3 caching) for sub-second retrieval.
Create Natural Language SQL Interfaces - Leverage Amazon Bedrock and LLM (large language models) to build a natural language-to-SQL generation tool that empowers analysts and stakeholders to query complex fraud datasets conversationally - no SQL expertise required.
Develop Real-Time Dashboards & Insights - Build rich, interactive dashboards using Grafana and internal tooling that provide real-time visibility into fraud trends, pipeline health, data quality metrics, and operational KPIs.
Curate a Living Data Dictionary - Design and maintain a comprehensive, searchable data catalog and dictionary that provides rich metadata, lineage, and documentation - improving discoverability and self-service analytics across the organization.
Champion Data Quality at Scale - Implement automated data validation, anomaly detection, and reconciliation frameworks that ensure accuracy, completeness, and consistency across the entire data lifecycle.
Secure & Govern Sensitive Data - Implement encryption, fine-grained access controls, audit trails, and compliance frameworks to protect highly sensitive payments and fraud data.
Monitor, Optimize, Repeat - Build observability into everything , proactive alerting, pipeline performance tracking, SLA monitoring, and intelligent auto-remediation to ensure five-nines data reliability.