Data engineering · Governance · AI-ready platforms
Smart data. Smarter decisions.
IntelliData AI builds governed, AI-ready data platforms. I design and deliver the pipelines, data quality, and cloud architecture that turn scattered source systems into analytics your leaders can trust.
Services
From raw source systems to trusted, AI-ready data.
Architect the foundation, engineer the pipelines, govern the data, and enable analytics and AI on top. Most engagements start with one area and grow.
Cloud & lakehouse architecture
Azure Databricks and AWS lakehouse designs on curated Delta tables and a reusable semantic layer, so analytics and AI share one trusted source.
Data warehouse & cloud migration
Move Teradata, Oracle, and on-prem warehouses to Redshift, Databricks, and Snowflake, with schema conversion, CDC, and no loss of trust in the numbers.
Data pipeline engineering
Ingestion, transformation, and orchestration on AWS Glue, Azure Data Factory, PySpark, and Step Functions, tuned for cost and reliability.
Metadata-driven frameworks
Reusable, metadata-driven ingestion with Data Vault and dimensional models, so new sources plug in fast and pipelines stay easy to maintain.
Data quality & governance
Critical data element identification, quality rules and registers, lineage, and audit and SOC readiness that closes findings for good.
Master data management
Golden-record MDM on Databricks and Redshift: entity resolution, cleansing, and a single, deduplicated view of your key data.
AI-ready data & GenAI enablement
Vector database and RAG foundations, embedding pipelines, and LLM-ready data using Bedrock and LangChain, so GenAI runs on data you can govern.
Analytics & BI enablement
Semantic layers, standardized KPIs, and Power BI and Tableau delivery, plus predictive models for spend, usage, and forecasting.
Fractional data leadership
Step in as senior data engineering leadership to set direction, mentor the team, and get delivery predictable again.
Where I help most
Three problems we are brought in to fix.
Data no one trusts
Reports disagree, audits keep reopening, and leaders second-guess the numbers. I make the data provably correct and keep it that way.
How I do itPlatforms not ready for AI
GenAI stalls without governed, well-modeled data underneath. I build the lakehouse, vector, and RAG foundations that make it real.
See the approachMigrations that stall
Legacy warehouses and slow batch windows hold the business back. I move them to the cloud without losing a number along the way.
Read case studiesOur work
From decisions to delivery.
Real engagements across manufacturing, healthcare, energy, insurance, logistics, and IT services. Client names are withheld.
Analytics platform for ServiceNow
A metadata-driven platform that broke past ServiceNow's native reporting limits, with Data Vault and dimensional models feeding Power BI.
Analytics and spend prediction for a utility
An in-house analytics platform that ended reliance on a third party and predicted spend and usage across 37+ customer types.
Golden-record MDM for a services firm
Entity resolution, cleansing, and deduplication built into a single trusted view of critical master data.
Commercial analytics lakehouse
Curated Delta tables and a reusable semantic layer for behavioral analytics, plus a RAG and vector proof of concept for knowledge discovery.
Industries
Where we work.
Deep experience in regulated, data-heavy industries, so we understand your systems and your constraints from the first conversation.
Insurance
Data quality, critical data elements, and audit and SOC readiness across policy and claims systems.
Healthcare
Modernized data warehouses and governed analytics in regulated, compliance-driven environments.
Energy & utilities
Analytics platforms and spend and usage prediction across large, varied customer bases.
Distribution & logistics
Scalable ingestion and analytics for high-volume distribution and relocation operations.
Manufacturing
Lakehouse foundations for behavioral analytics, predictive models, and GenAI readiness.
IT & digital services
Unified analytics across ITSM and digital infrastructure tools, from one metadata-driven platform.
Writing
Notes on data work.
Turning audit findings into a data quality program
An audit finding is a symptom. Here is how I turn a list of findings into something that keeps the data clean for good.
What a "critical data element" should actually mean
Most CDE lists are too long to be useful. A shorter test for what earns a place on the list.
Why your lakehouse migration keeps slipping
The delays are rarely technical. They come from decisions no one wanted to own.
Contact
Tell us what you're working on.
A focused assessment, a migration, a data quality program, or an AI-ready platform. Send a note and we will tell you a practical next step.