Architect, Data Modelling

NeoSapients Inc.; Bengaluru; On-site; INR 1,800,000 - 2,800,000; Full time

Job summary

NeoSapients Inc. is seeking an Architect – Data Modelling to lead semantic design across enterprise data landscapes. You will model data for analytics, BI, and AI agents, covering relational schemas, semantic layers, ontologies, and knowledge graphs. The role emphasizes scaling data models for real-world business use cases and agent workflows.

You will collaborate with engineering, applied science, and product teams to ensure governance, quality, and practical adoption of data architectures.

Qualifications

• 5-7 years of experience in data modelling, data architecture, or a related data-centric role.
• Proven expertise in semantic data models and abstraction layers serving analysts, BI platforms and AI systems.
• Strong command of dimensional modelling methodologies (Star Schema, Snowflake, Data Vault).
• Hands-on experience with knowledge graphs and graph databases (Neo4j, Amazon Neptune).
• Advanced SQL proficiency for model validation, performance analysis, and data quality.
• Exposure to AI/ML and agentic systems, including LLM data consumption patterns and RAG architectures.
• Excellent communication and stakeholder management to present complex models.

Responsibilities

• Conduct enterprise-wide discovery across source systems and data domains to map the data landscape.
• Design semantic data models, canonical entities, and shared vocabularies for analytics, business, and AI systems.
• Architect dimensional, analytical, and semantic models to support reporting and agent-driven workloads.
• Build and maintain semantic layers, knowledge graphs, ontologies, and taxonomies for contextual understanding.
• Design AI-ready data structures and metadata-rich models for Retrieval-Augmented Generation and autonomous decision-making.
• Collaborate with Product, Data Engineering, Applied Science, and Agentic AI teams to support real-world use cases.
• Validate model designs through workshops, use-case mapping, sample queries, and agent simulations.
• Establish data governance standards, lineage, naming conventions, and modelling best practices.

Skills

Semantic data models

Data architecture

Dimensional modelling

Star Schema

Snowflake

Data Vault

Knowledge graphs

Graph databases

Neo4j

Amazon Neptune

SQL

BI tools (Power BI, Tableau, Looker)

RAG architectures

Data governance

Stakeholder management

Tools

Neo4j

Amazon Neptune

Power BI

Tableau

Looker

SQL

Job description

Location: Bangalore

Contract Type: Full Time

Department: Engineering / Data & AI

Job Title: Architect – Data Modelling

Location: Bangalore

Job Type: Full-time

Experience: 5-7 years

Department: Context Platform Engineering

Reporting To: Principal, Contextual AI

About Us

We’re an early-stage, high-impact startup building the next generation of Agentic AI Platforms - a future where autonomous agents work alongside humans to accelerate innovation, decision-making, and execution across industries.

Backed by a passionate founding team, cutting-edge technology, and a bold vision, we’re looking for a product leader who thrives in ambiguity, embraces ownership, and is hungry to build something game-changing from the ground up.

Role Overview

This is not a traditional data modelling role.

You will be the person who looks at the enterprise data landscape and asks: "How should the enterprise data, processes, and knowledge be semantically modelled so that an AI agent can reason over it, a BI analyst can slice it, and an end user can get a meaningful answer, all from the same underlying model?"

You will own the semantic and structural design of data models that serve a dual audience: humans who consume insights and agents that act on context.

Your canvas spans relational schemas, semantic layers, ontologies, and knowledge graphs, and your north star is always the functional outcome the end user is trying to achieve.

Key Responsibilities

• Conduct enterprise-wide discovery across source systems, data domains, ownership structures, relationships, and quality gaps to establish a comprehensive understanding of the data landscape.
• Design semantic data models, canonical entities, and shared business vocabularies that enable consistent interpretation across analytics, business, and AI systems.
• Architect dimensional, analytical, and semantic models using methodologies such as Star Schema, Snowflake, and Data Vault to support reporting, self-service analytics, and agent-driven workloads.
• Build and maintain semantic layers, knowledge graphs, ontologies, and taxonomies that enable contextual understanding, semantic search, and intelligent reasoning.
• Design AI-ready data structures, agent-readable schemas, and metadata-rich models optimized for Retrieval-Augmented Generation (RAG), agent memory, and autonomous decision-making.
• Collaborate with Product, Data Engineering, Applied Science, and Agentic AI teams to ensure data models effectively support real-world business use cases and agent workflows.
• Validate model designs through stakeholder workshops, functional use-case mapping, sample queries, reporting prototypes, and agent simulations to ensure practical adoption and scalability.
• Establish and enforce data governance standards, quality rules, lineage documentation, naming conventions, and modelling best practices across the platform.

Qualifications

• 5-7 years of experience in data modelling, data architecture, or a related data-centric role with a strong understanding of enterprise data ecosystems.
• Proven expertise in designing semantic data models and abstraction layers that serve analysts, BI platforms, business users, and AI systems through a unified architecture.
• Strong command of dimensional modelling methodologies including Star Schema, Snowflake, and Data Vault, with the ability to apply the right approach based on business and performance requirements.
• Hands-on experience with knowledge graphs, graph databases (Neo4j, Amazon Neptune, or similar), and familiarity with ontology or taxonomy design principles.
• Experience conducting data discovery, stakeholder interviews, source system analysis, and developing comprehensive data landscape documentation.
• Advanced SQL proficiency for model validation, query optimization, performance analysis, and data quality assessment, along with familiarity with BI tools such as Power BI, Tableau, or Looker.
• Exposure to AI/ML and agentic systems, including an understanding of LLM data consumption patterns, RAG architectures, vector retrieval, and graph-based retrieval techniques.
• Strong communication and stakeholder management skills, with the ability to present complex data models and architectural concepts to both technical and non-technical audiences.