Past Work
Real problems.
Measurable results.
A selection of data engineering and AI projects built across AWS — each one focused on a real business problem and a result you can point to.
GenAI Competitive Intelligence Agent
The Problem
Sales team spent hours manually researching competitors before each deal — no consistent, real-time data in CRM.
What Was Built
AI agent using AWS Bedrock + microservices that automatically delivers real-time competitive intelligence directly into Salesforce CRM.
50% reduction in research time. Improved seller win rates across AWS accounts.
Cloud Data Lake Modernization
The Problem
Legacy data infrastructure couldn't handle real-time event streams. Analytics ran on stale, batch-processed data.
What Was Built
End-to-end analytics solution with real-time streaming pipelines using EventBridge, Kinesis Firehose, and Glue — feeding a partitioned S3 data lake with Lake Formation governance.
Moved from batch to real-time. Full data governance layer. Scalable to any volume.
Enterprise Analytics Dashboard Suite
The Problem
Leadership had no unified view of business metrics. 30+ ad-hoc SQL queries run manually every week.
What Was Built
50+ complex SQL datasets and 30+ interactive dashboards with star schemas in AWS Redshift, surfaced via Amazon QuickSight.
40% faster query performance. Self-serve analytics — leadership gets answers without waiting on the data team.
Real-time Streaming Analytics Platform
The Problem
Millions of events per day processed in nightly batches — meaning insights were always 24 hours stale.
What Was Built
Streaming analytics platform using Kinesis Data Streams, Lambda, and DynamoDB processing millions of events in real time.
Sub-second latency for real-time insights. Team moved from daily reports to live dashboards.
Automated Data Quality Framework
The Problem
Downstream reports regularly failed due to bad source data — no automated validation layer in the pipeline.
What Was Built
Comprehensive data quality monitoring system using AWS Glue DataBrew and custom Python validators across enterprise datasets.
99.9% data accuracy. Downstream failures eliminated. Trust restored in the reporting layer.
AWS Cost Optimization Engine
The Problem
Cloud costs growing faster than revenue. No visibility into which services or teams were driving spend.
What Was Built
Intelligent cost analysis tool using AWS Cost Explorer API, automated recommendations, and Tableau dashboards for spend visibility.
30% cost reduction. Engineering teams had clear ownership of their spend for the first time.
Customer Sentiment Analysis Pipeline
The Problem
Customer feedback came in from 5+ channels — no unified view, no way to spot issues early.
What Was Built
End-to-end sentiment analysis pipeline using AWS Comprehend and custom ML models, processing feedback from all channels into a single dashboard.
Real-time sentiment visibility. Support team could surface and respond to issues hours earlier.
Enterprise Data Catalog
The Problem
Engineers spent hours finding the right dataset — no central catalog, no metadata, no data ownership tracking.
What Was Built
Comprehensive data catalog using AWS Glue Data Catalog and custom metadata management, with a self-serve search UI.
70% improvement in data discovery time. Clear data ownership and lineage across the org.
Want results like these
for your data stack?
Let's talk about your current setup and what it would take to get your marketing data working as hard as you do.