Open to architecture consulting & senior data integration roles

I build AI-powered data & integration systems that scale in production

Workflow automation, enterprise integrations, and mission-critical pipelines engineered for reliability—from Paris 2024-scale security data to multi-cloud commerce integrations.

Context & scale

Paris 2024
AWS
Azure
Alibaba Cloud
Comcast
Eurocontrol

Graph view · Obsidian-style · drag nodes · hover highlights links

Engineer. Integrator. Builder.

Moving from shipping features to designing systems means owning scale, failure modes, and change—so pipelines stay reliable when traffic, vendors, and requirements shift.

Nagesh R Shanbhag focuses on AI-assisted delivery, data integration, and automation: from Paris 2024–scale security data to commerce and ERP integrations—turning complex inputs into operable, monitored flows.

Explore expertise

Core competencies

Where Nagesh Shanbhag designs, directs, and ships high-leverage integrations and automation.

Work together →

AI & Automation

LLM-assisted delivery, Jira and workflow automation, QA automation, and Datakeon—natural language to workflow execution.

Data Engineering

Apache NiFi clusters, ETL/ELT, streaming security and commerce data, reusable frameworks for reliability.

System Integration

ERP and commerce integrations (Workato, n8n, REST), multi-cloud ingestion, and stakeholder-led delivery.

Technical strengths

Languages

Core JavaPythonSQL

Data & integration

Apache NiFin8nWorkatoETL/ELTREST APIsSystem integrations

AI & automation

LLM integrationPrompt engineeringAI-assisted developmentJira automationWorkflow automationQA automation

Cloud

Azure (ADF, Databricks, ADLS)AWS (VPC, WAF, CloudWatch, Inspector V2)

Databases

MongoDBAzure SQL

DevOps

GitLabJenkinsDockerKubernetes

Systems in action

Selected programs spanning Olympic-scale security data, multi-cloud ingestion, commerce integrations, and product work.

Founder project

Datakeon — Founder / Product

AI workflow automation: turn natural language into runnable pipelines—execution engine, monitoring, and migration from legacy integrations.

LLM orchestrationIntegration frameworkIn progress
Dashboard and data visualization

More case studies

Paris 2024 Olympics Middleware

Atos (Eviden)

Large-scale security data pipelines integrating AISaac MDR with Alibaba Cloud; NiFi HA clusters, Java processors for parse/transform/route, Grafana monitoring.

Apache NiFiJavaGrafana

Multi-Cloud Security Pipelines

Atos

Unified ingestion from AWS, Azure, Alibaba Cloud, and Akamai WAF; production deployments for enterprise clients including Comcast and Eurocontrol.

Multi-cloudNiFiEnterprise

Commerce Data Integrations

Allure Commerce

ETL for Adobe Commerce and Microsoft Business Central; ERP integrations via NiFi, n8n, Workato, and REST APIs.

NiFiWorkatoREST

Rennai (Canada)

Allure Commerce

End-to-end Workato integration delivered in record time with reliable cross-system sync.

WorkatoIntegration

Gliks (Canada)

Allure Commerce

End-to-end architecture, planning, stakeholder communication, and delivery ownership.

ArchitectureDelivery

Professional journey

From Olympic-scale security data pipelines to leading integration delivery for global commerce clients.

Manager – Data Integration

Allure Commerce LLP, Goa

Current role

Sep 2024 – Present

Leads a team of data integration specialists; owns client engagements, solution design, and delivery. Drives AI-assisted architecture, Jira/workflow/QA automation, Workato delivery (e.g. Rennai), and full ownership of Gliks.

Team leadershipWorkatoAI-assisted deliveryStakeholder management

Associate Engineer

Atos (Eviden), Bangalore

Sep 2022 – Sep 2024

Mission-critical integrations for Paris 2024 Olympics security data; NiFi pipelines across AWS, Azure, Alibaba, Akamai WAF; custom Java processors; HA clusters and reusable frameworks.

Apache NiFiJavaMulti-cloudSecurity data

Let's build something reliable

Hiring, consulting, or collaboration—reach out directly or use the assistant to learn more and propose a time from there when it fits.