Data Architect (Data Modelling and Databricks)

Job Title: Data Architect (Data Modelling Databricks)

Location: All LTM Office Locations

Experience: 16-19 Years

Job Summary

We are seeking a highly experienced Data Architect with deep expertise in Data Modelling and Databricks , along with exposure to advanced AI/GenAI solutions. The ideal candidate will lead the design and implementation of scalable data architectures, modern data pipelines, and AI-driven solutions aligned with enterprise goals.

Key Responsibilities

Data Modelling Architecture (60%)

Design and implement logical and physical data models using 3NF, Data Vault 2.0, and Star/Snowflake schemas
Perform data re-architecture for scalability, performance, and maintainability
Develop parent-child relationships across complex data structures
Work on geospatial data modelling (WKT, WKB formats)
Ensure adherence to data governance and modelling standards

Solution Architecture

Apply architecture patterns such as Lambda and Kappa architectures
Design solutions for batch and streaming data processing
Define and document architecture blueprints, reference models, and integration patterns
Align solutions with business goals and technology roadmaps

GenAI Advanced AI Integration

Lead Generative AI and Agentic AI initiatives
Design and develop RAG (Retrieval-Augmented Generation) pipelines
Build and implement AI agents (sequential, parallel, loop-based architectures)
Develop and deploy AI-powered solutions using Databricks and enterprise platforms

Technical Environment

Work hands-on with Databricks (certifications preferred)
Develop data pipelines, reusable components, and orchestration workflows
Integrate data solutions with APIs, AI agents, and BI tools

Required Skills

Strong expertise in Data Modelling (3NF, Data Vault, Star/Snowflake Schema)
Hands-on experience with Databricks and data engineering workflows
Experience in data architecture design and large-scale data platforms
Knowledge of streaming and batch processing architectures (Lambda/Kappa)
Exposure to GenAI, RAG pipelines, and AI agent frameworks
Strong understanding of data governance and performance optimization

Roles Expectations

Provide technical leadership and architectural guidance
Collaborate with cross-functional stakeholders
Drive innovation in data and AI architecture

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