GCP LOOKER

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Please note that the tech stack would be TDC/GCP related with Looker (new) for semantic modeling, visualization. They would need to know GitHub and pipeline deployment methods.

The Data Modeler is a strategic, hands-on expert responsible for designing, developing, and governing enterprise-wide data models that form the foundation of the organization’s analytics, reporting, integration, and data governance initiatives.

This role leads the development of conceptual, logical, and physical data models across domains, enforces data modeling best practices, and ensures the alignment of all data models to the enterprise data strategy. Working closely with business domain counterparts, architects, data engineers, and analytics teams, the Data Modeler ensures stable, scalable, and governed data architecture to support business transformation and digital innovation.

Key Responsibilities :

Enterprise Data Modeling & Architecture

 Develop and maintain enterprise conceptual, logical, and physical data models for core business domains.

 Lead data model design sessions, ensuring alignment with business goals and IT strategy.

 Define modeling standards and reusable patterns across teams and projects.

Data Governance &aStandards;

 Establish and enforce enterprise data standards, naming conventions, and data definitions.

 Drive metadata management practices to ensure consistent interpretation of data across systems.

 Collaborate with governance stewards and domain owners to ensure data quality and integrity.

Domain Collaboration &Business; Alignment





 Act as the reliable IT data modeling counterpart to business domain teams across key functions (e.g., finance, supply chain, operations, sales).

 Translate business requirements into scalable and governed data models.

 Work closely with business analysts and product owners to validate model semantics and usability.

Data Integration & System Design

 Design and document data integration patterns across systems (ERP, CRM,

data lake, warehouse, etc.).

 Align data models to support MDM, data mesh, data fabric, and data product strategies.

 Support data engineers in the implementation of physical data structures.

Tool Utilization & Modeling Platform Expertise

 Use leading data modeling tools to manage data models and metadata.

 Develop and maintain a central data model repository and version control process.

Performance Optimization

 Partner with DBAs and data engineers to optimize data structures for performance, scalability, and maintainability.

 Review query and ETL performance and propose model-based improvements.

Documentation &Communication;

 Document and communicate data models, data lineage, and relationships with clarity.

 Serve as a key communicator of data architecture strategy and modeling outcomes.

Coaching & Mentorship

 Mentor data modelers, data engineers, and analytics professionals in modeling standards and practices.

 Review and approve models created by other teams to ensure enterprise alignment.

Continuous Improvement & Research

 Stay abreast of industry trends, modeling techniques, and emerging technologies.

 Continuously improve modeling practices and tools based on lessons learned and stakeholder feedback.

📌 GCP Looker (Bengaluru)
🏢 Teamware Solutions
📍 Bengaluru