JASOBANTA DAS

Solution Architect | AWS • Databricks • Snowflake • Machine Learning | Data & Analytics

+1-248-757-9364 • Jasobanta.das.bi@gmail.com • Texas, USA • www.jasobantadas.com

About

With 17+ years of experience, I design and deliver enterprise-grade data platforms that help organisations unlock the true value of their data.

From cloud-native Data Lakes and Lakehouse architectures to real-time streaming pipelines and GenAI solutions — I bridge the gap between complex technology and measurable business outcomes.

I lead solution architecture at TCS, working with global enterprise clients across Healthcare, Banking, Insurance, and Finance — building the data foundations that power smarter decisions, faster.

Core Expertise

Cloud Data Platform Architecture
AWS | Databricks Lakehouse | Snowflake | scalable, governed, and enterprise-ready data platforms
Data Architecture & Specialization
Enterprise Data Platform | Lakehouse Design | Data Warehousing | Medallion Architecture | Data Modeling | Consumption Layer Design
Big Data & Streaming Architecture
Apache Spark | Kafka | Kinesis | Delta Live Tables | Hadoop | batch and real-time data processing frameworks
Machine Learning & AI Architecture
ML Solution Architecture | ML Pipelines | Feature Engineering | Model Deployment | MLOps | Predictive Analytics
Data Engineering & Integration
End-to-end pipeline design | ETL/ELT Architecture | API-driven integration | Cloud-native and on-premise | Informatica IICS
Governance, Security & Metadata
Unity Catalog | AWS Glue Catalog | RBAC | Metadata Management | Data Quality | Lineage | Lifecycle Management
DevOps, CI/CD & Platform Automation
Terraform | Jenkins | GitHub Actions | Infrastructure as Code | CI/CD | deployment and environment automation
Performance, Scalability & Cost Optimization
Workload optimization | compute strategy | storage design | performance tuning | cost-efficient architecture

Specializations

Machine Learning Specialization
  • Designed end-to-end ML architecture aligned with business goals, scalability, and production readiness.
  • Defined ML use cases — classification, regression, forecasting, and anomaly detection — based on business value and data feasibility.
  • Designed data preparation, feature engineering, and feature management strategies for reliable model development.
  • Defined model selection approaches based on problem type, performance needs, explainability, and operational constraints.
  • Established structured training, validation, and testing architecture to ensure model quality and generalization.
  • Designed reusable pipelines for data processing, training, evaluation, deployment, and retraining.
  • Defined batch and real-time inference architecture for integrating ML models into enterprise applications and analytics platforms.
  • Established versioning, reproducibility, automation, governance, and lifecycle controls for enterprise ML operations.
  • Designed monitoring frameworks for model accuracy, data drift, performance degradation, and operational reliability.
  • Defined interpretability, transparency, and controlled usage standards to support trusted and governed ML adoption.
Databricks Specialization
  • Architected a unified Databricks platform for governed, scalable, and business-aligned analytics.
  • Designed enterprise governance, metadata, lineage, and secure access architecture.
  • Established reusable Databricks architecture standards and implementation blueprints.
  • Defined scalable architectural patterns for both scheduled and near real-time pipelines.
  • Designed reliable, monitored, and maintainable data pipeline frameworks.
  • Improved platform efficiency through compute, storage, and workload redesign.
Snowflake Specialization
  • Designed scalable and governed Snowflake platforms for enterprise data warehousing, analytics, and data consumption.
  • Defined database, schema, and layer segregation models for domain-based organization and controlled access.
  • Implemented role-based access control, privilege hierarchy, and ownership strategy for secure platform governance.
  • Established Dev, QA, UAT, and Prod separation for controlled releases and platform stability.
  • Designed secure internal and external data sharing models for governed cross-team and cross-domain collaboration.
  • Defined warehouse sizing, workload isolation, and scaling strategy for concurrent and cost-efficient workloads.
  • Created reusable standards, naming conventions, and design patterns for consistent Snowflake implementations.
  • Architected reliable and maintainable pipelines for ingestion, transformation, orchestration, and operations.
  • Improved efficiency through query tuning, clustering strategy, warehouse right-sizing, and workload optimization.
  • Designed curated and governed serving layers for reporting, analytics, and downstream integrations.

Additional

ENTERPRISE CLIENT & PRODUCT EXPERIENCE

Cigna Healthcare

Marriott International

GE

Deutsche Bank

Nationale-Nederlanden

Goldman Sachs

Dell International

INDUSTRIES

Healthcare

Banking

Insurance

Finance

Retail

Manufacturing

WHY WORK WITH ME

Let's connect if you're modernising your data platform, building a Lakehouse, or exploring GenAI solutions.

Jasobanta.das.bi@gmail.com • www.jasobantadas.com • Texas, USA