Data Architect

Key Responsibilities

• Design and build enterprise Data & AI platforms across hybrid environments, integrating
  
  

on-premise platforms (e.g., Cloudera) with modern cloud services (e.g., Azure).

• Define enterprise architecture for data platforms, streaming systems, and AI workloads to
  
  

enable scalable analytics and real-time data processing across banking domains.

• Architect and implement enterprise event streaming platforms using Confluent Kafka,
  
  

enabling real-time integration across core banking, digital channels, and enterprise

applications.

• Design Digital Data Store (DaaS) platforms (e.g., on Azure Cosmos DB) to serve
  
  

enterprise data domains such as Customer, Accounts, Loans, Cards, Wealth, and

Transactions.

• Build domain-based data products and data marts supporting Customer 360, Wealth 360,
  
  

and enterprise analytics use cases.

• Design and implement enterprise AI and GenAI platform architectures using cloud-native
  
  

AI services (e.g., Azure OpenAI Service, Azure AI Foundry) for secure and governed AI

workloads.

• Define enterprise AI reference architectures, including LLM integration, RAG patterns,
  
  

vector search pipelines, and secure API-based AI services.

• Design MLOps architecture using Databricks MLflow and Azure ML, enabling model
  
  

lifecycle management, experiment tracking, and scalable AI deployment.

• Implement GenAI solutions, such as NLP-to-SQL data assistants and internal AI chatbots,
  
  

for natural language interaction with enterprise data platforms.

• Represent the Data & AI Architecture domain in enterprise architecture forums and AI
  
  

governance councils, contributing to AI standards and technology strategy.

Technical Skills & Tech Stack

• Data Platforms & Architecture: Data Lakes, Lakehouse, Real-time Streaming Platforms,
  
  

Data Warehouse, NoSQL, Data Modelling, Data Governance.

• Big Data & Streaming: Cloudera (CDP), Confluent Kafka, Databricks.
• Cloud Infrastructure (Azure prioritized): Azure ADLS, Azure Data Factory, Azure
  
  

Synapse, Azure Databricks, CosmosDB (Mongo vCore), Azure DevOps.

• AI/ML & GenAI: Azure OpenAI, Azure AI Foundry, MLflow, Azure ML, LLM integration,
  
  

RAG patterns.

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