Cloud Data Analytics & ML Architect Portfolio
JASOBANTA DAS
Solution Architect | AWS • Databricks • Snowflake • AI/ML | Data & Analytics
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.
TOGAF CertifiedAWS Solutions Architect – Professional
$ phone: +1-248-757-9364
$ email: Jasobanta.das.bi@gmail.com
$ location: Texas, USA
$ linkedin: jasobanta-das-496b1389
$ github: jdas86
$ website: www.jasobantadas.com
Top 3 Impact
17+ years of experience
Top 3 Impact
Led migrations: AWS, Databricks, Snowflake, ML Analytics
Top 3 Impact
Architected end to end — from whiteboard to production
Core Expertise
Designed and provided solutions across cloud, data, ML, governance, and platform scale
Every skill group below is taken directly from the source profile and preserved as portfolio content.
Cloud Data Platform Architecture
AWSDatabricks LakehouseSnowflakescalable, governed, and enterprise-ready data platforms
Data Architecture & Specialization
Enterprise Data PlatformLakehouse DesignData WarehousingMedallion ArchitectureData ModelingConsumption Layer Design
Big Data & Streaming Architecture
Apache SparkKafkaKinesisDelta Live TablesHadoopbatch and real-time data processing frameworks
Machine Learning & AI Architecture
ML Solution ArchitectureML PipelinesFeature EngineeringModel DeploymentMLOpsPredictive Analytics
Data Engineering & Integration
End-to-end pipeline designETL/ELT ArchitectureAPI-driven integrationCloud-native and on-premiseInformatica IICS
Governance, Security & Metadata
Unity CatalogAWS Glue CatalogRBACMetadata ManagementData QualityLineageLifecycle Management
DevOps, CI/CD & Platform Automation
TerraformJenkinsGitHub ActionsInfrastructure as CodeCI/CDdeployment and environment automation
Performance, Scalability & Cost Optimization
Workload optimizationcompute strategystorage designperformance tuningcost-efficient architecture
Experience
Architecture leadership presented as a premium transformation narrative
The source resume explicitly names one company and multiple specialization domains. This layout keeps the facts intact while presenting them with stronger depth and storytelling.
Impact Highlights
17+ years of experience designing and delivering enterprise-grade data platforms.Led complex migrations: AWS, Databricks, Snowflake, ML Analytics.Improved platform efficiency through compute, storage, and workload redesign.Improved efficiency through query tuning, clustering strategy, warehouse right-sizing, and workload optimization.
Solution Architect — TCS
May 2021 - Present • USA
I work as solution architect at TCS, working with global enterprise clients across Healthcare, Banking, Insurance, and Finance — building the data foundations that power smarter decisions, faster.
I own architectures end to end — from whiteboard to production.
I've led enterprise data transformation across AWS, Hadoop, Hive, Snowflake, and Databricks — driving complex migrations, architecting modern Lakehouse platforms, and shaping resilient data engineering and governance strategy.
Machine Learning Specialization
ML Solution ArchitectureML PipelinesFeature EngineeringModel DeploymentMLOpsPredictive Analytics
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
Databricks LakehouseGovernanceMetadataLineageBatchStreaming
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
SnowflakeRBACWarehousingData SharingOptimization
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.
Achievements
Metrics, migrations, optimization, and enterprise-grade operating rigor
Only achievements grounded in the source are highlighted here.
17+ years of experience designing and delivering enterprise-grade data platforms.
Premium Impact Signal
Led complex migrations: AWS, Databricks, Snowflake, ML Analytics.
Premium Impact Signal
Improved platform efficiency through compute, storage, and workload redesign.
Premium Impact Signal
Improved efficiency through query tuning, clustering strategy, warehouse right-sizing, and workload optimization.
Premium Impact Signal
Established versioning, reproducibility, automation, governance, and lifecycle controls for enterprise ML operations.
Premium Impact Signal
Defined scalable architectural patterns for both scheduled and near real-time pipelines.
Premium Impact Signal
Enterprise Client & Product Experience
Trusted across global enterprise names
Cigna HealthcareMarriott InternationalGEDeutsche BankNationale-NederlandenGoldman SachsDell International
Industries
Cross-sector architecture experience
HealthcareInvestmentBankingInsuranceFinanceRetailManufacturing