NeoSapients Inc.
Website:
inctoinc.org
Company:
https://www.linkedin.com/company/neosapients-inc
Seniority: Not Applicable
Industries: Software Development
Job details:
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Architect, Data Modelling
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Location
Bangalore
Contract Type
Full Time
Department
Engineering / Data & AI
Job Title: Architect – Data Modelling
Location: Bangalore
Job Type: Full-time
Experience: 5 to 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.
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