---
title: "Chittiappa Areyada - Senior Data Engineering Manager in Bengaluru | Tal"
description: "Meet Chittiappa Areyada, Senior Data Engineering Leader | Data Platform Architect | Enterprise Lakehouse & AI-Ready Platforms | Ex-HSBC, Maybank based in Bengaluru. AI stack includes claude code, claude, and gemini."
image: "https://tal.club/meet/chittiappa-areyada/share-image"
---

# Chittiappa Areyada on Tal

## Headline

Senior Data Engineering Leader \| Data Platform Architect \| Enterprise Lakehouse & AI\-Ready Platforms \| Ex\-HSBC, Maybank

## About

Strategic, business\-focused Senior Data Engineering Leader and Platform Architect with 15\+ years of success designing petabyte\-scale Data Lakehouse environments and steering high\-performing global engineering teams across tier\-1 financial institutions\. Recognized expert in Google Cloud Platform \(GCP\) data design, FinOps cloud cost governance, and building enterprise\-grade, AI\-ready feature platforms to accelerate GenAI and Machine Learning frameworks\. Trusted advisory partner to global C\-suite, Risk, and Finance executives, translating complex cross\-functional business needs into secure, scalable, cost\-efficient data architectures that align directly with stringent international regulatory frameworks \(BCBS 239, IFRS, IRB\) and corporate revenue milestones

## Location

Bengaluru

## AI stack

claude code

claude

gemini

openai

## Work experience

Senior Data Engineering Manager at NAM Info Inc, May 2026 \- Present · 6 mos, Bengaluru, Karnataka, India, 🚀 Project: Enterprise AI & ML Feature Pod Platform Problem: Disconnected feature engineering steps across teams were delaying Machine Learning \(ML\) and Generative AI time\-to\-market and blowing out GCP compute limits\. Impact: Architected a modular Feature Pod ecosystem on Google Cloud\. This enabled rapid pipeline reuse across teams, cutting model deployment timelines by 40% and ensuring complete governance for LLM applications\. 🏗️ Project: Petabyte\-Scale Medallion Lakehouse Transformation Problem: Fragmented data sources caused complex pipelines, high query latencies, and unreliable downstream analytics data\. Impact: Led the end\-to\-end design and deployment of a secure GCP Medallion Data Architecture\. Unified batch and real\-time processing streams, which slashed overall processing latency by 30% and reduced query costs through automated warehouse partitions\. 👥 Initiative: Global Engineering Delivery & Stakeholder Alignment Problem: Lack of centralized engineering standards across cross\-functional domains caused delayed compliance reporting and unpredictable software release cycles\. Impact: Provided cross\-functional technical management to global agile engineering squads\. Unified delivery roadmaps across Risk, Analytics, and Finance teams, accelerating pipeline delivery velocity by 25% while mentoring junior managers

Assistant Vice President – Data Engineering Architect at HSBC, Sep 2021 \- Apr 2026 · 4 yrs 8 mos, Bengaluru, Karnataka, India, 🏛️ Project: Cross\-Region GCP Data Lakehouse Modernization Problem: Fragmented on\-premises infrastructure delayed critical IFRS and regulatory reporting timelines across UK, MENA, and ASP banking sectors\. Impact: Architected and scaled a centralized enterprise GCP Data Lakehouse platform\. Re\-engineered batch and near\-real\-time streaming infrastructure using BigQuery, Dataflow, and Dataproc, which boosted reporting processing speeds by 35% and ensured strict compliance with global data standards\. 🛡️ Project: Decentralized Data Mesh Quality & Lineage Automation Problem: Lack of unified data lineage and automated controls across global regions exposed the bank to high operational and regulatory data risks\. Impact: Engineered and automated a centralized Data Mesh governance framework\. Built proactive data quality monitoring, automated lineage tracking, and metadata cataloguing across 3 global regions, eliminating manual business\-as\-usual \(BAU\) checks by 80% and mitigating data compliance risks\. 🛡️ Project: Enterprise Global Data Quality Automation & Governance Framework Problem: Fragmented data ownership and manual quality checks across multiple global banking regions created operational friction and heightened compliance risks under strict financial regulations\. Impact: Led the strategic design and automation of an enterprise\-grade Data Mesh governance framework on GCP\. Established clear data ownership, unified metadata catalogs, and automated data lineage tracking across UK, MENA, and ASP regions\. Results: Migrated legacy on\-premises data quality platforms to a proactive cloud\-native model, eliminating manual business\-as\-usual \(BAU\) data quality checks by 80%, mitigating regulatory compliance risks, and giving C\-suite stakeholders complete confidence in reporting data\.

Senior Manager – Data Engineering Lead at Maybank, Sep 2018 \- Aug 2021 · 3 yrs, Bengaluru, Karnataka, India, 🏗️ Project: Regional Credit Risk Data Lake Consolidation Problem: Siloed cross\-border data systems delayed critical computing timelines for complex regulatory calculations \(such as Expected Credit Loss \[ECL\], PD, and LGD parameters\) across APAC markets\. Impact: Led the product engineering and architecture of a centralized Enterprise Credit Risk Data Lake\. Unified batch ingestion framework lifecycles across diverse legacy source systems, creating a single source of truth for IFRS 9, IRB, and Stress Testing data streams\. Results: Streamlined month\-end financial reporting cycles across multiple countries while providing full lifecycle visibility from ingestion to governance\. ⚙️ Project: Automated Reconciliation & Back\-Testing Engine Problem: Missing real\-time validation checks within risk reporting streams caused data mismatches between risk and finance analytics\. Impact: Developed and deployed automated pipeline verification and back\-testing analytics engines using high\-performance Python and SQL scripts\. Results: Eradicated downstream data mismatches, accelerated stress\-testing audit trails, and achieved 100% adherence to financial compliance standards during external audit sweeps\. 👥 Initiative: Cross\-Functional Risk\-Finance Data Architecture Strategy Problem: Misaligned tech roadmaps between distributed data teams and chief risk officers frequently delayed product updates and regulatory submissions\. Impact: Supervised the complete agile delivery lifecycle by partnering directly with Risk, Finance, and Analytics business leaders to define strict data definitions and capabilities\. Results: Spearheaded technical mentoring programs across the broader engineering team, helping scale up overall data warehouse standards and data product quality\.

Lead – Data Engineering & Analytics Platform Engineer at Satwic Inc, Aug 2010 \- Aug 2018 · 8 yrs 1 mo, Bengaluru Area, India, 🎬 Project: High\-Volume Media Analytics & ETL Pipeline Transformation Problem: Massive daily spikes in transactional media data from global business lines caused data delivery delays, stalling critical financial dashboards and daily executive reports\. Impact: Architected, designed, and optimized high\-performance, end\-to\-end ETL/ELT data pipelines using Informatica, DataStage, and SQL Server for large\-scale data ingestion and aggregation\. Results: Eliminated downstream data latency, ensured consistent data availability for corporate financial reporting, and enabled executive teams to make fast, data\-driven decisions\. ⚙️ Project: Data Quality Control Automation & Query Optimization Problem: Unpredictable source data formats and unoptimized SQL queries led to pipeline pipeline instability, processing bottlenecks, and frequent reporting SLA failures\. Impact: Designed and deployed robust automated data validation and control frameworks to check data accuracy, completeness, and consistency before loading\. Led global query optimization initiatives across the core database systems\. Results: Dramatically enhanced overall data warehouse reliability, slashed pipeline compute resources, and achieved a 99\.9% SLA adherence rate for all corporate reporting data\. 👥 Initiative: High\-Availability Platform Support & Engineering Mentorship Problem: Inefficient incident tracking and lack of clear documentation on legacy codebases slowed down data platform support, threatening global business operations\. Impact: Led the production support engineering team, establishing agile tracking standards, runbooks, and proactive alerting mechanisms\. Personally mentored junior engineers on database tuning, SQL optimizations, and delivery best practices\. Results: Significantly reduced mean\-time\-to\-resolution \(MTTR\) for critical incidents, improved pipeline stability, and successfully leveled up the delivery output of the engineering squad\.

## Education

M\.C\.A, Computer Science, New Horizon College of Engineering

BCA, Computer Science, Sri Dharmasthala Manjunatheshwara College, Ujire\-574240, Belthangady

[View the original profile](https://tal.club/meet/chittiappa-areyada)

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