Experience.

Designed and built a consolidated enterprise data lake on AWS EMR and Apache Iceberg for a major research university — turning complex on-premises Workday and PeopleSoft data into queryable, relational tables through an auto-relationalization engine.

• Designed and built an auto-relationalization engine in Python and Spark that converts complex Workday and PeopleSoft XML into queryable Iceberg tables — automatically, end-to-end.
• Replaced Airflow with an event-driven Step Functions / EventBridge / Lambda architecture for the Student Data Warehouse — dynamic DAG generation, no orchestration server to maintain.
• Stood up a Bedrock-powered Lambda for autonomous CloudWatch metrics analysis; reduced API call volume by 98% through BU-filtered queries.
• Implemented complete infrastructure-as-code using Terraform: 50+ Lambda functions, 20+ Step Functions workflows, full data governance framework.

Architected the strategic AWS data-platform-as-a-service originating in Marketing and progressively adopted by roughly ten additional internal teams on the trajectory toward enterprise-wide use.

• Re-architected platform ingestion to serverless (Step Functions, Lambda, EventBridge) and migrated to elastic compute — shifting the cost profile from always-on infrastructure to consumption-aligned, while improving performance.
• Built an LLM-based pipeline-failure triage system that identifies root causes and recommends remediation — meaningfully reducing time-to-diagnosis for the operations team.
• Implemented Bedrock AI agents for automated PII and sensitive-data classification across the data lake at 95%+ accuracy.
• Established Lake Formation fine-grained access controls and a metadata-driven onboarding model — new sources go live in days instead of weeks.

Designed and implemented sub-second microservice architecture, plus the GCP migration target architecture and AI-assisted CI/CD model.

• Architected a shared-sandbox selective-promotion CI/CD model that supports dozens of engineers across multiple teams without trampling each other.
• Designed the future-state GCP target architecture for sub-second microservices migrating from AWS.
• Implemented and managed cloud network and security primitives — subnetworks, security groups, routing, segmentation.

Built Taco Bell's first ground-up AWS analytics environment — VPC, security, data lake, scheduling.

• Replaced an expensive commercial scheduler with a custom Lambda + Step Functions orchestration framework that handles automatic restartability and failure recovery.
• Architected a real-time streaming analytics platform on Kinesis, Lambda, and DynamoDB processing restaurant POS data into operational insights.
• Stood up centralized observability on CloudWatch + Elasticsearch + Kibana for real-time pipeline health visibility.

Consulting solution architect for Hashmap (and NTT Data, post-acquisition) engagements at Poly/Plantronics, SEI Investments, and Juniper Networks — three-month modernization and migration engagements, Snowflake security design, and serverless AWS data-platform patterns.

• Led modernization and migration engagements across multiple client environments — orchestrated proofs of concept, guided clients on "buy vs. build" decisions on tooling and architecture, and produced three-month modernization plans with the supporting design documents, resourcing, and project plans. Served as solution, data, and cloud architect on on-premises-to-cloud analytics transitions.
• Designed Snowflake security and DDL management frameworks — roles, resource monitors, warehouses, users, role grants, and operating documentation — including row-security policies for the secure exposure of multi-tiered financial data to a wide array of privileged audiences. Defined column- and row-level security patterns for AWS using Pulumi, Glue, Lake Formation, and Athena, in support of self-service serverless distributed data platforms.
• Designed and developed AWS-based serverless microservices using application load balancers, certificates, JWT tokens, and Step Functions, plus Python Lambda ingestion utilities for moving data into S3 and Snowflake. Specialized work included GDPR-compliant Lambda utilities for a healthcare client.
• Designed real-time integrations from operational source systems into Snowflake and led subject-area reconciliation — facilitating mappings, business interviews, scoping, modeling, and augmentation of the consumption layer. Established DevOps practices and Snowflake patterns advancing data-lake-centric and ELT-centric architectural designs.
• Designed Microsoft Power BI solution architecture — high-level strategy, not hands-on — to support row-level security in Snowflake across thousands of users.