Data Modeler - Telecom Data & Information Architecture

Summary

Data Modeler - Telecom Data & Information Architecture (Remote)

Location: United States (Remote)

Employment Type: Full-Time

About Cognizant

Cognizant is one of the world's leading professional services companies, helping clients modernize technology, reimagine processes, and transform experiences. We engineer modern businesses to improve everyday life. Our associates around the globe help clients build stronger, more agile, and innovative organizations.

Our Data, AI, and Cloud practice helps organizations build modern data foundations that support advanced analytics, artificial intelligence, digital transformation, and operational excellence. We partner with industry leaders to develop scalable data architectures, enterprise data models, governance frameworks, and intelligent data platforms that enable better business decisions and innovation.

Role Overview

Cognizant is seeking an experienced Data Modeler to support the design and development of scalable telecom data models that serve as the foundation for analytics, reporting, AI, and operational use cases.

In this role, you will work closely with Telecom Subject Matter Experts and Data Engineering teams to translate complex business concepts into standardized conceptual, logical, and physical data models. You will play a key role in defining telecom entities, business relationships, hierarchies, and data structures while ensuring models are implementation-ready for enterprise-scale data platforms.

The ideal candidate will have strong expertise in data modeling, metadata management, business analysis, and telecommunications data domains, along with experience validating data models using synthetic and representative datasets.

Work Model

This position is fully remote within the United States , offering flexibility while collaborating with business, domain, architecture, and engineering teams across multiple locations.

Occasional travel may be required for workshops, planning sessions, or strategic stakeholder meetings.

Key Responsibilities

Enterprise Data Modeling

+ Develop and maintain:

+ Conceptual Data Models

+ Logical Data Models

+ Physical Data Models

+ Design scalable data models aligned with business, architecture, analytics, and AI requirements.

+ Define and document:

+ Entities

+ Attributes

+ Relationships

+ Hierarchies

+ Business Rules

+ Keys and Constraints

+ Ensure consistency across subject areas and enterprise data domains.

Telecom Domain Modeling

+ Partner closely with Telecom Domain Experts to understand:

+ Business Processes

+ Service Lifecycles

+ Customer Data Structures

+ Product Hierarchies

+ Operational Workflows

+ Network Data Relationships

+ Translate telecom business concepts into standardized enterprise data structures.

+ Define reusable data models supporting operational and analytical use cases.

+ Support development of enterprise information architecture standards.

Synthetic Data Design & Validation

+ Collaborate with Data Engineering teams to create representative synthetic datasets.

+ Define fabricated data scenarios that accurately represent business conditions and use cases.

+ Validate:

+ Data Structures

+ Relationships

+ Business Rules

+ Data Types

+ Cardinality

+ Hierarchy Logic

+ Use synthetic data testing to refine and improve data models before production data availability.

+ Support iterative model validation and enhancement activities.

Data Mapping & Transformation Analysis

+ Map source data structures to target enterprise data models.

+ Identify:

+ Data Gaps

+ Data Quality Issues

+ Transformation Requirements

+ Standardization Opportunities

+ Data Inconsistencies

+ Collaborate with engineering teams to ensure successful implementation of target-state models.

+ Support source-to-target mapping and transformation documentation efforts.

Analytics & AI Readiness

+ Design models that support:

+ Business Intelligence

+ Reporting

+ Advanced Analytics

+ Machine Learning

+ AI & GenAI Use Cases

+ Ensure models support scalability, reusability, and future business requirements.

+ Partner with architecture and data engineering teams to align data structures with enterprise AI initiatives.

Metadata & Documentation Management

+ Create and maintain:

+ Data Dictionaries

+ Business Glossaries

+ Metadata Definitions

+ Mapping Documents

+ Data Standards

+ Model Documentation

+ Maintain traceability between business requirements and data structures.

+ Ensure documentation reflects current-state and target-state architectures.

Collaboration & Governance

+ Work closely with:

+ Telecom Domain SMEs

+ Data Engineers

+ Architects

+ Business Analysts

+ Analytics Teams

+ AI Teams

+ Participate in data governance and architecture review processes.

+ Support data standardization and enterprise data management initiatives.

+ Contribute to long-term information architecture strategies and best practices.

Required Qualifications

+ Bachelor's degree in Computer Science, Information Systems, Data Science, Engineering, or a related discipline, or equivalent experience.

+ Proven experience as a:

+ Data Modeler

+ Information Architect

+ Data Architect

+ Enterprise Data Analyst

+ Strong expertise developing:

+ Conceptual Data Models

+ Logical Data Models

+ Physical Data Models

+ Experience defining:

+ Entities

+ Relationships

+ Business Rules

+ Hierarchies

+ Metadata Structures

+ Strong understanding of:

+ Data Modeling Methodologies

+ Normalization Techniques

+ Dimensional Modeling

+ Information Architecture

+ Experience performing:

+ Data Mapping

+ Gap Analysis

+ Data Validation

+ Metadata Management

+ Strong documentation and analytical skills.

+ Excellent communication and stakeholder collaboration abilities.

Preferred Qualifications

+ Telecommunications industry experience.

+ Knowledge of telecom business domains including:

+ Customer Management

+ Product Management

+ Service Management

+ Network Operations

+ Billing Systems

+ Order Management

+ OSS/BSS Platforms

+ Experience designing synthetic datasets for validation and testing purposes.

+ Familiarity with ente...