Founder-led data platform engineering

Data foundations built for decisions, automation and AI.

We help growing teams replace fragile pipelines, uncontrolled cloud spend, and untrusted reporting with a governed platform they can actually operate.

Book a platform audit See the work
Layered cloud data platform with real-time data streams
Platform viewConnected and observable
Built into your environment
IngestGovernModelServe
8+ yearsData engineering leadership
Founder-ledTechnical discovery and direction
Your cloudDelivery inside your environment
Your codeDocumented ownership at handover
Built for data-intensive teams
  • Financial services
  • Healthcare
  • Retail
  • Manufacturing
  • SaaS
  • Logistics
How we help

From platform friction to production confidence.

Start with the constraint that matters most. We audit, modernize, and optimize the system as one connected foundation.

Data platform architecture layers
Platform audit Architecture health
01 / Diagnose

Platform Audit

Expose reliability gaps, quality risk, governance debt, and cloud waste before you commit to a rebuild.

Explore the audit
Cloud data platform with connected data streams
02 / Modernize

Platform Build

Implement real-time ingestion, dbt models, quality controls, governance, and observability inside your cloud.

See platform solutions
Data platform performance, security, and cost controls
FinOps control Cost follows value Workload-level visibility
03 / Optimize

Reliability + FinOps

Reduce waste, improve workload performance, and give teams the operating model to keep the platform healthy.

View optimization work
The standard

Data work should feel like infrastructure,
not firefighting.

One connected foundation

Every layer designed to reinforce the next.

Architecture is only useful when it survives production. We connect source systems, ingestion, transformation, storage, governance, and consumption as one operating system.

Connected data platform architecture from source systems to analytics Production architecture
  1. 01
    SourceOperational systems, APIs and files
    Postgres · MySQL · SaaS
  2. 02
    MoveBatch, CDC and real-time streaming
    Debezium · Kafka · ADF
  3. 03
    ModelTested transformation and lineage
    dbt · Spark · Quality
  4. 04
    StoreCost-aware cloud data foundations
    Snowflake · Databricks · Fabric
  5. 05
    UseTrusted metrics, analytics and AI
    Power BI · Looker · ML
Selected outcomes

Measured in production.

Read all case studies
Real-time data · Financial services 8×

faster data delivery

Nightly batch dependencies replaced by a governed real-time foundation.

 Cloud FinOps · Retail 62%

lower Azure data spend

Workload visibility, right-sizing, and a repeatable internal FinOps practice.

 Analytics engineering · SaaS 94%

fewer data incidents

Fragile SQL replaced by tested, documented, maintainable data models.

Engagements are anonymized under NDA. Baselines, scopes, and measurement details are documented in each case study.

Prabhu Saravanan, CEO and Founder of DataFortis
Direct technical ownership
“The recommendation should come from the person accountable for the architecture.”

Your first conversation is with Prabhu Saravanan, founder and data architect. Discovery, technical direction, and delivery decisions stay connected from the first audit to handover.

Prabhu SaravananCEO & Founder · 8+ years in data engineering
Meet the founder
Secure by design

Your environment.
Your controls.

SOC 2-alignedAccess control, audit logging, and change management
GDPR-readyData minimization, residency awareness, and DPA support
Least privilegeEncrypted flows and identity-first delivery patterns
Owned by youCode, documentation, and infrastructure stay in your accounts
Before we begin

Common questions.

Clear expectations before any engagement starts.

How long does a typical engagement take?

Most engagements run 4–16 weeks. Focused audits or CDC pilots can be completed in 2–4 weeks; full platform builds typically take 8–16 weeks.

Which platforms do you specialize in?

Azure, AWS, Snowflake, Databricks, BigQuery, and Delta Lake, supported by dbt, Debezium, Kafka, Airflow, Spark, and modern observability tools.

Do you work with companies outside India?

Yes. DataFortis works globally with overlap for US East and Europe. Architecture sessions, documentation, and code reviews are conducted in English.

Will we own the code and infrastructure?

Yes. Code, Terraform, dbt models, dashboards, and documentation are delivered into your repositories and cloud account, with operational handover and training.

Start with clarity

Make your data platform ready for what comes next.

Bring the architecture, cost, reliability, or AI-readiness question. Leave with a concrete next step.

Book a platform audit info@datafortis.in