MJMonish Jain Let's talk
Enterprise Data Architect · MBA

Turning fragmented data into trusted foundations for analytics and AI.

I help organizations modernize complex data environments, align enterprise data models, and build cloud data platforms on Databricks, Snowflake and Azure. Twenty years of it, across healthcare, insurance, pharma, utilities and manufacturing.

Discuss an assessment → Explore my experience ↓

New York City Metro · open to US remote roles and advisory work

SOURCE SYSTEMS Claims Eligibility Pharmacy ERP · CRM Bronzeraw, as landed · full history Silverconformed 3NF · common data model Golddimensional · KPIs · semantic models Power BI · Tableau Self-service BI AI agents · RAG GOVERNED BY: MDM · QUALITY · LINEAGE · SECURITY
DATA ARCHITECT · 20+ YEARS
20+ yrsin data architecture, warehousing and BI
10organizations, from KPMG to GEHA
6industries, led by healthcare and insurance
Who I work with

Two ways to work with me.

For organizations

Independent advice on where your data platform should go next.

  • Warehouse-to-lakehouse modernization plans
  • Enterprise and common data models across systems
  • Governance, MDM and data quality foundations
  • AI-powered analytics on governed, AI-ready data
See the Health Check →
For hiring teams

A senior architect who designs the model and builds the platform.

  • Principal and Enterprise Data Architect roles
  • Healthcare and insurance data architecture
  • Cloud data platform and lakehouse architecture
  • Data architecture lead for delivery teams
See my experience ↓
What I do

From business question to working data platform.

My work sits between business strategy and technology: understanding what stakeholders need, making architecture decisions I can defend, and helping teams deliver.

Architecture

Enterprise data modeling

Conceptual, logical and physical models. OLTP in 3NF and OLAP in star and snowflake schemas, using Kimball and Inmon.

ERwin · ER/Studio · common data models
Modernization

Lakehouse and cloud platforms

Moving legacy warehouses to the cloud with Medallion architecture and modern table formats.

Databricks · Snowflake · Azure · Delta Lake · Iceberg
Integration

Pipelines and migration

Source-to-target mapping, ETL/ELT design and migrations, including two utility divestitures and a global SAP consolidation.

ADF · SSIS · Informatica · PySpark
Governance

Trusted, governed data

Master and reference data, quality rules, metadata, lineage and standards, plus role-based access, row-level security and masking for sensitive data.

MDM · data quality · lineage · RBAC · RLS
Analytics

BI foundations

Dimensional and semantic models, KPIs and dashboards that make self-service reporting possible.

Power BI · Tableau · SSRS
Leadership

Evaluation and delivery

Proofs of concept, platform selection, architecture roadmaps, vendor management and globally distributed teams.

PoC / PoT · Agile · mentoring
Consulting offer · Data Architecture Health Check

A clear view of your data platform, and a 90-day plan to improve it.

An independent assessment of how your data architecture is built today, where the risks are, and which changes will matter most.

  1. 01

    Map

    A current-state architecture diagram of your sources, platforms, data models and reporting.

  2. 02

    Prioritize

    A ranked list of risks across modeling, integration, governance, security and performance.

  3. 03

    Plan

    A 90-day roadmap that sequences the changes with the biggest impact first.

Discuss an assessment →
AI-powered analytics

Putting AI to work on your data, on a foundation you can trust.

AI can speed up analysis, answer business questions in plain language and surface patterns people miss. It only works when the data underneath is well modeled, governed and secure. That is where I start.

Ask in plain English

Conversational analytics

Let business users ask questions of governed data and get answers grounded in your semantic models and agreed KPI definitions.

Faster insight

AI-assisted data analysis

Use AI to profile data, flag anomalies and trends, and draft findings that analysts review and refine.

Foundations

AI-ready data

Semantic models, metadata, lineage and business definitions that make enterprise data understandable to AI agents and RAG.

Guardrails

Secure, compliant AI

Role-based access, row-level security and PHI/PII masking carried into AI workflows, so every answer respects who is asking.

Governed data→Semantic model & KPIs→Access controls→AI analytics & agents→Trusted business answers
Case studies

Selected work.

Health insuranceOngoing

An enterprise lakehouse for healthcare claims

Challenge
Move a SQL Server and Synapse warehouse to the cloud and merge claims from several processing systems.
Approach
Databricks Medallion layers; common data models for medical, dental and pharmacy claims; a Snowflake dimensional model with KPIs; role-based access, row-level security and column masking for PHI and PII.
Result
One governed claims and coverage foundation for enterprise reporting, built for HIPAA compliance.
Pharmacy benefitsMainframe modernization

Choosing a distributed database, with evidence

Challenge
Pick an application database for a microservices replacement of an IBM mainframe claims system.
Approach
Installed and ran a YugabyteDB proof of concept across three private-cloud data centers; built the enterprise data model; compared modeling tools and warehouse options.
Result
A platform decision grounded in tested criteria instead of vendor claims.
UtilitiesSnowflake · Azure

A cloud warehouse for a natural-gas utility

Challenge
Bring work management, customer service and financial data together, and support two divestitures.
Approach
Snowflake warehouse with facts, dimensions and roles; Azure Data Factory pipelines; inbound and outbound migrations.
Result
Power BI KPIs for customer service, safety and finance, plus real-time emergency dashboards.
PharmaField sales analytics

Replacing spreadsheets for a pharma sales team

Challenge
Field reps and managers relied on Excel reports to track performance.
Approach
A secure star-schema warehouse with role-based data restriction; Tableau Server with SAML sign-on; a re-architected prescriber call plan feeding the CRM.
Result
Performance and year-over-year views by team, product, territory and region.
Experience

Twenty-four years, ten organizations.

A path from hands-on data warehousing to enterprise architecture, cloud platforms and governance.

2024 – now

Data Architect

GEHA · health insurance

Lakehouse modernization on Databricks and Snowflake; common claims data models; Snowflake dimensional model; PHI/PII security strategy for HIPAA compliance.

2022 – 2024

Senior Data Architect

SS&C Technologies · DST Health

Mainframe-to-microservices modernization on private cloud, YugabyteDB evaluation, enterprise data model for pharmacy claims, Power BI semantic models.

2018 – 2022

DW / BI Architect

South Jersey Industries · utilities

Snowflake warehouse, Azure Data Factory, emergency-metrics dashboards, two divestiture migrations.

2016 – 2018

Data / BI Architect

Bausch Health · pharma

Secure reporting warehouse, Tableau and Power BI for field sales, MDM and corporate DW.

2013 – 2016

Information Architect

Axalta Coating Systems · manufacturing

DW strategy for global sales and profitability, SAP consolidation, integration across SAP, Salesforce and Oracle.

2012 – 2013

Data Architect

KPMG · audit

eDataMining redesign; processing cut from over an hour to under three minutes; scale-up and scale-out strategies.

2011 – 2012

Data Warehouse Architect

Lincoln Financial Group · retirement

Retirement and pension warehouse enhanced to integrate a new asset management system; BusinessObjects self-service BI.

2010 – 2011

Data Warehouse Architect

AXIS Capital · insurance

Enterprise warehouse for policy, claims and cash, built on the IBM IIW industry data model.

2009 – 2010

Data Architect

EmblemHealth · health insurance

Led the Assurance of Discontinuance project, keeping online provider information current as mandated by the New York Attorney General.

2002 – 2009

Data Architect

Philadelphia Insurance Companies

Transactional and dimensional data architecture for a specialty property and casualty insurer: policy administration, billing and commissions, and claims.

Industries Healthcare & health insurance Insurance & financial services Pharmacy benefits Pharma & life sciences Utilities Manufacturing
Monish Jain
About Monish

I started in computer science and added an MBA. That pairing still shapes how I work: every model I design has to answer a business question.

Over twenty years I have designed warehouses, lakehouses and analytics platforms for health insurers, a pharmacy-claims processor, a global manufacturer, a utility and an audit firm. I take a practical, hands-on approach, balancing enterprise standards with what teams can actually build and maintain.

Today I am most interested in what comes next: how well-governed enterprise data, semantic models and metadata can make AI applications and agents trustworthy.

MBALeBow College of Business, Drexel University
BS, Computer ScienceGujarat University, India
Insights

Building the data foundations for enterprise AI.

AI is only as trustworthy as the data beneath it. These are the questions I am writing about.

AI-ready data

Making enterprise data understandable to AI agents

Why semantic models, business definitions and metadata matter more once an agent is asking the questions.

Essay coming soon
Governance

Permissions, lineage and quality in AI workflows

Extending the governance you already have to retrieval and agentic patterns.

Essay coming soon
Modernization

What a Medallion lakehouse gets right, and where it needs help

Lessons from modeling claims across bronze, silver and gold layers.

Essay coming soon
Contact

Let's talk about your data architecture.

Whether you are modernizing a warehouse, unifying data models, strengthening governance or preparing for AI, I welcome conversations with technology leaders, hiring teams and organizations planning what comes next.

Message me on LinkedIn ↗ linkedin.com/in/monishjain