I design data platforms that hold up as you grow.
I help enterprises modernize their data estate, keep it governable, and get it ready for AI - then leave their team able to run it. Fourteen years doing this hands-on, across Azure, AWS, and GCP.
- Ingestion, governance, AI consumption
- End-to-end
- Multi-cloud delivery
- Azure · AWS · GCP
- You work with the architect, directly
- Independent
Most data platforms were built for a problem that has since changed.
A lot of teams are now running AI on top of platforms designed for batch reporting. That mismatch is usually where the friction starts.
- 1990s
Data Warehouse
Centralize for reporting
Rigid schemas, batch-bound
- 2010s
Data Lake
Store everything cheaply
Swamps, no governance
- 2018
Lakehouse
Unify lake + warehouse
Still centrally owned
- 2021
Data Mesh
Decentralize ownership
Hard to operationalize
- Now
AI-native systems
Built with AI in mind
Where things are heading
You work directly with the person doing the work.
Daksh Bharat is an independent practice. I design your architecture, I sit with your team, and I am the one who hands it over in a state they can own.
Gaurav Gupta
Founder & Principal Architect
I have spent 14+ years designing enterprise data platforms that scale without rewrites, and building the teams behind them. Cloud-native architect across Azure, AWS, and GCP, with delivery spanning regulated and data-intensive industries. I started Daksh Bharat to do this work directly, without the layers - solving the problem and leaving your team able to own and extend it long after I am gone.
Connect on LinkedInCertifications
- Microsoft Certified: Azure Solutions Architect Expert
- AI-102 Azure AI Engineer
- DP-203 Data Engineering on Azure
- PL-300 Power BI Data Analyst
- SAFe 6 Practitioner
Designing enterprise data platforms
Led data engineering & data science orgs
Azure, AWS, and GCP delivery
Growth headroom designed in, without rewrites
Industries delivered in
- Healthcare & Life Sciences
- Insurance (Life, Property & Specialty)
- Marketing & Sales Analytics
- Contact Center Analytics
- SaaS & Data Products
- EdTech & Online Assessment
- Telecom
- Financial Services
Architectures that survive an order of magnitude of growth. Teams that level up and start setting their own bar for excellence. Spotting tomorrow's tech without chasing shiny objects. Turning data, and now AI, from a cost center into a revenue lever.
A few principles I keep coming back to.
Domain Ownership
The teams closest to the data own it end-to-end: definition, quality, and delivery.
Data as Product
Datasets are versioned, discoverable, documented products with SLAs - not tickets.
Federated Governance
Policy is encoded once and enforced everywhere. Governance becomes an enabler.
Platform Thinking
A self-serve platform removes friction so domains ship without central bottlenecks.
AI Readiness
Lineage, semantics, and access are engineered for agents and models from day one.
This is the lens I bring to every engagement, before a line of the design is drawn.
See how I apply themA handful of things I hold to, after fourteen years of doing this work.
- 01
Architecture should outlast the architect.
If a platform wobbles the moment I step away, I have not finished the job. I design for the team that has to run it on a Tuesday, not for the diagram.
- 02
New technology has to earn its place.
I am not interested in adopting something because it is new. It has to solve a problem you actually have, or it stays out of the design.
- 03
Solving the problem is only half of it.
The other half is leaving a team that thinks differently about the work and is equipped to carry it forward without me.
A reference architecture you can click through.
Rather than describe it in prose, here it is as a diagram. Step through each layer to see how raw sources become governed, AI-ready data products.
From legacy data to an operating model your team can run.
Five steps I move through on most engagements. Less a rigid methodology, more a way of staying honest about sequence.
Observe
We instrument your data estate, score Architecture Debt, and baseline AI readiness across domains.
The kinds of problems I take on.
Each one walks through the problem, the architecture, the approach, and where it landed. Details are anonymized - the shape of the work is real.
Diagnose your architecture debt in 60 seconds.
Answer six questions. We quantify the accumulated complexity slowing your AI ambitions - and what it takes to reverse it.
01Who owns data quality across your organization?
02How long to deliver a new governed dataset to a consumer?
03How is data governance enforced today?
04Can AI/ML teams access governed, lineage-tracked data?
05How many distinct data platforms are in production?
06How often do pipeline changes break downstream consumers?
0 of 6 answered
We start with the truth
- Your estate as it is
- Debt quantified
- Trade-offs named
I design for your team
- A target operating model
- Something you can run
- No black boxes
We move incrementally
- Strangler migration
- Governed by design
- No big-bang rewrites
Your team takes it forward
- The thinking transfers
- Documented and owned
- I step back
See where your architecture stands today.
A short self-assessment across architecture debt, AI readiness, and governance. Run it on your own, or walk through it with me.