Abhi M.

Enterprise Cloud Data Architect with 13+ years of experience leading enterprise-scale data transformation, cloud modernization, and advanced analytics initiatives across AWS and Azure ecosystems. Expertise in architecting scalable lakehouse, real-time streaming, metadata-driven, and event-driven data platforms that enable enterprise analytics, AI/ML, governance, and data-driven decision-making. Proven track record of delivering measurable business outcomes through platform modernization, real-time insights, automation, cost optimization, governance, and data democratization initiatives. Trusted advisor to C-level executives, business stakeholders, and engineering leaders in defining enterprise data strategy, establishing architecture standards and governance frameworks, accelerating cloud adoption, and delivering scalable, secure, and highperforming data platforms. Experienced in leading multiple data modernization programs and building resilient, future-ready ecosystems that support operational excellence, customer intelligence, regulatory compliance, and business growth. Recognized for mentoring high-performing global teams, fostering engineering excellence, and aligning technology strategy with longterm business objectives. SKILLS: Cloud Data Architecture & Strategy: Principal Data Architecture, Enterprise Data Architecture, Cloud Data Architecture (AWS, Azure), Multi-Cloud Architecture, Data Platform Strategy, Enterprise Data Modernization, Cloud Migration Strategy, AWS Well-Architected Framework, Architecture Governance, Architecture Review Board (ARB), RFP/RFQ Solution Design, Data Mesh Concepts, Lakehouse Architecture, Solution Architecture at Scale Cloud Platforms & Data Services: AWS: Amazon S3, Amazon Redshift, AWS Glue, AWS Lambda, Amazon Kinesis, AWS EventBridge, AWS Step Functions, AWS Lake Formation, AWS IAM, AWS CloudWatch, AWS Data Catalog Azure: Azure Data Factory, Azure Databricks, Delta Lake, Delta Live Tables (DLT), Unity Catalog, Lakeflow Connect, Databricks Workflows, Spark-based Lakehouse Engineering. Data Engineering & Processing: Apache Spark, PySpark, Spark SQL, ETL/ELT Pipelines, Streaming Data Processing, Real-Time Data Architecture, Batch Processing, Distributed Data Processing, Change Data Capture (CDC), Apache Iceberg, Data Lakehouse Architecture, High-Volume Data Pipelines. Data Governance, Security & Compliance: Data Governance, Data Security Architecture, Data Lineage, Data Quality Management, Metadata Management, Data Observability, Unity Catalog (Governance & Access Control), IAM (Identity & Access Management), Data Cataloging, Regulatory Compliance (GDPR), Encryption at Rest and In Transit, Policy-Based Data Access. Generative AI & Intelligent Systems: Generative AI Architecture, Agentic AI, Retrieval-Augmented Generation (RAG), Vector Search Systems, LangChain, OpenAI API, Prompt Engineering (Enterprise Use Cases), Semantic Search Architectures, LLM-Based Applications, ChromaDB, FAISS FastAPI, Django, Streamlit. DevOps, Automation & DataOps: CI/CD for Data Platforms, GitHub Actions, AWS CloudFormation, Infrastructure as Code (IaC), Docker, Python Automation, SQL Optimization, DataOps, Automated Deployment Pipelines, Infrastructure Automation