Cloud Data Architect

Architect and implement scalable solutions using Snowflake and Databricks on AWS and Azure

Design and implement AI and Gen AI solution for data value chain

Design data integration pipelines (batch, real-time, big data) and analytics platforms

Define and implement data governance, quality, metadata, and line age frameworks and should be able to leverage GenAI capabilities.

Act as a trusted advisor to senior business and IT stakeholders

Architect Agentic AI ecosystems using LLMs, vector databases, and orchestration frameworks (LangChain, AutoGen, CrewAI).

Define Model Context Protocol (MCPs) to chain reasoning, retrieval, and action models.

Design Agent-to-Agent (A2A) communication protocols for collaborative multi-agent workflows.

Implement retrieval-augmented generation (RAG) pipelines with memory, context management, and tool usage.

Must Have Technical/Functional Skills

Architect enterprise data platforms for data lake, Lakehouse, streaming systems.

Design data integration and data pipeline patterns

Should be able to evaluate new technologies and run proof of concepts.

Should be able to set data and AI strategy for data organization.

Established data Quality, lineage and metadata standards

Ensured compliance with privacy, security and regulation

Drives adoption of responsible AI frameworks

Created architectural guardrails

Drive consensus on standards (eg data contracts, lineage) across different data organizations

Reviews design and elevate architectural thinking across teams

Creates reusable patterns, templates and reference architectures

Very strong understanding and experience on Data products, data mesh and Medallion Architecture implementation

Design and implement AI and Gen AI solution for data value chain

Strong experience with LLMs, prompt engineering, and agent frameworks (LangChain, AutoGen, CrewAI).

Deep understanding of MCPs, ReAct, Tree of Thought, and AutoGPT-style reasoning.

Hands-on with Python, OpenAI APIs, Anthropic Claude, Vector DBs (FAISS, Pinecone, Weaviate).

Experience with A2A orchestration, agent memory strategies, and tool calling.

Strong grasp of enterprise architecture, data governance, and security protocols.

Experience with cloud platforms (Azure, AWS, Google Cloud Platform) and MLOps pipelines.

Very strong understanding and experience on Data products, data mesh and Medallion Architecture implementation

Implement retrieval-augmented generation (RAG) pipelines with memory, context management, and tool usage.

Define Model Context Protocol (MCPs) to chain reasoning, retrieval, and action models.

Hands-on with Python, OpenAI APIs, Anthropic Claude, Vector DBs (FAISS, Pinecone, Weaviate)., 15-20 years of experience in data architecture, data engineering, and analytics platforms

Strong consulting experience in large BFSI transformation programs

Hands-on expertise with Snowflake and Databricks (Lakehouse architecture)

Design and implement AI and Gen AI solution for data value chain

Very strong understanding and experience on Data products, data mesh and Medallion Architecture implementation

Experience with cloud data services in aws,azure,Google Cloud Platform

Strong background in data integration, reporting, and big data ecosystems

Experience working in regulated environments with data governance and compliance requirements

Excellent stakeholder communication and leadership skills