VIRESH GENDLE • PRINCIPAL DATA ARCHITECT

Transforming Rigid Data Warehouses into Agile Lakehouses.

I architect systems that handle Enterprise-Scale complexity while achieving significant infrastructure optimization and cost efficiency. Specializing in rescuing failed migrations and implementing FinOps-native data platforms.

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ROI
Driven Data Architecture
40%+ Avg. Cost Optimization
High-Vol Data Architected
Critical Latency Reduction
Zero Data Loss Migrations
25+ Enterprise Deployments
8+ Years Operating

Engineering Principles

Lakehouse > Warehouse

Decoupling compute from storage is non-negotiable. I migrate clients off rigid, expensive warehouses (Snowflake/Synapse SQL) to open Delta Lake architectures, enabling infinite scaling at a fraction of the cost.

Data Quality as Code

Bad data is worse than no data. I implement "Great Expectations" into every pipeline. If the schema drifts or nulls spike, the pipeline breaks intentionally before it pollutes the Gold layer.

FinOps Native

Performance without cost-control is negligence. Every architecture I design includes auto-scaling rules, spot instance leverage, and aggressive partition pruning strategies from Day 1.

Technical Expertise

01

Data Analytics & BI

Architecting semantic layers and high-performance tabular models. Optimizing DAX for billion-row datasets and financial reporting.

Power BI SSAS Tabular Python T-SQL DAX
02

Azure Data Engineering

Designing Medallion Architectures (Bronze/Silver/Gold). Implementing Lambda Architectures for hybrid batch/streaming ingestion.

Azure Databricks Data Factory Synapse Event Hubs Delta Lake
03

MLOps & Production AI

Automating model training pipelines with Drift Detection. Deploying scalable inference endpoints on Kubernetes.

MLflow Azure ML Docker AKS FastAPI
04

DevOps & Infrastructure

Managing Infrastructure as Code (IaC). Implementing CI/CD pipelines for DataOps reliability.

Terraform Azure DevOps GitHub Actions ARM Templates Grafana
05

LLM Orchestration

Building RAG architectures and Multi-Agent Systems. Optimizing context windows and vector retrieval latency.

OpenAI API LangChain Pinecone Streamlit Azure AI Search
06

Data Integration

Handling High-Throughput streaming ingestion (Kafka/Event Hubs). Ensuring Data Quality protocols (Great Expectations).

Apache Spark Kafka PySpark REST APIs Unity Catalog

Architecture Case Studies

NDA Notice: The following are architectural case studies of enterprise projects I have delivered. Specific client data cannot be shared. Codebase reviews available upon request.
01
Cloud Migration & Optimization

Enterprise Lakehouse Transformation

Re-architected a rigid, failing SQL Warehouse for a Global Retailer. The system was crashing under 12TB daily loads with 4-hour query latency.

THE HARDEST PART Handling 40% data skew during the primary join of sales & inventory tables. Standard Spark shuffles were causing OOM errors. Implemented usage of Salted Joins and Broadcast variables to distribute load evenly.
OUTCOME: Zero-downtime migration • Improved query performance • Significant Annual Savings.
🔒 Architecture Under NDA
02
Generative AI / RAG

Secure Enterprise RAG System

Designed an internal LLM search engine for 50k+ legal documents. Client needed "ChatGPT-like" answers but with zero data leakage and strict RBAC.

THE HARDEST PART Context Window Optimization. Naive RAG was retrieving irrelevant chunks, confusing the LLM. I architected a "Hybrid Search" (Keyword + Vector) with a re-ranking step to boost precision from 45% to 92%.
OUTCOME: 80% reduction in document discovery latency • Deployed to 500+ users • Zero Security Incidents.
🔒 Architecture Under NDA
03
MLOps & Production AI

Real-Time Fraud Detection Pipeline

End-to-end MLOps platform for a Fintech client. Needed to score transactions in <200ms while handling 10k TPS.

THE HARDEST PART Feature Store Consistency. The online inference store (Redis) was drifting from the offline training store (Delta Lake). I implemented a "Feature Store" pattern to guarantee point-in-time correctness for defined features.
OUTCOME: Fraud detection accuracy up 15% • Latency sustained at 120ms (p99) • Prevented Multi-Million Dollar Fraud Losses.
Architecture Under NDA
04
Big Data Infrastructure

Enterprise Infrastructure Migration

Executed a zero-downtime migration of on-premise Legacy Data Warehouses to Azure Synapse & Databricks.

THE HARDEST PART Global Schema Consistency. 500+ tables across 10 business units had conflicting data types. I architected a "Schema-First" automated mapping engine to ensure 100% parity before the final cutover.
OUTCOME: Multi-TB Datasets Migrated • Decomissioned 12 physical servers • Significant Operational Efficiency Gained.
🔒 Architecture Under NDA

Technical Validation Protocol

Claims of "Petabyte Scale" or "Million-Dollar Annual Savings" are audited using a rigorous Verification Framework. I provide the evidence behind the architecture to guarantee ROI.

💰 Financial Impact Audit
01

Baseline Cost Benchmarking

Extensive audit of pre-project Azure/AWS spend using specialized tools to identify idle resources and cost leakage.

02

Post-Migration Delta Tracking

Continuous performance monitoring vs infrastructure spend to calculate the exact ROI of optimized query profiles.

03

Stakeholder Cross-Ref

All savings reports are cross-verified with client FinOps and Operations teams for total accuracy.

🛡️ Data Integrity Protocol
04

Automated Parity Checks

Implementation of "Great Expectations" frameworks to ensure source-to-lake parity at the row level.

05

Shadow Pipeline Validation

Running legacy and new systems in parallel for 30 days to ensure zero data drift before decommission.

06

Distributed Checksumming

For Petabyte-scale moves, every chunk is verified via checksums to guarantee 100% data fidelity.

Architecture Decision Records (ADR) and Benchmark Reports are provided with every engagement.

Let's Build Something Amazing

Ready to transform your data and AI capabilities? Let's discuss your requirements and explore how we can deliver measurable business value through innovative technology solutions.

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Krishnav Tech – Enterprise AI Solutions

Specialized AI & Data Engineering Practice

Krishnav Tech is a specialized Data & AI practice focused on scaling high-performance infrastructure for enterprise clients. We combine deep technical expertise with business acumen to build scalable, production-ready systems that drive real business value.

Our approach is outcome-driven, with a focus on measurable ROI and long-term sustainability. We don't just build technology—we solve business problems through intelligent data solutions.

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