Enterprise Data Modeler

Key Responsibilities

Data Modeling & Design

Develop conceptual data models to define business entities, relationships, concepts, and rules.

Build logical and physical data models for complex client hierarchies and document data designs.

Present and communicate modeling results and recommendations to stakeholders and development teams.

Governance & Standards

Establish and enforce governance processes for data modeling and implementation activities.

Define and maintain enterprise-wide standards for data models, naming conventions, and metadata.

Support Master Data Management (MDM) practices including reconciliation, deduplication, and integration of source systems.

Analysis & Optimization

Perform data profiling and analysis to establish, modify, and maintain models.

Analyze data-related system integration challenges and propose strategic solutions.

Conduct data capacity planning, life cycle management, feasibility studies, and optimization assessments.

Integration & Architecture

Develop canonical models, Data-as-a-Service (DaaS) models, and leverage SOA for integrations.

Support enterprise integration initiatives with modern data platforms (e.g., Snowflake, Azure Synapse, Databricks).

Ensure compliance with data privacy regulations, security policies, and regulatory requirements.

Data Quality & Security

Implement processes to monitor and improve data quality, accuracy, completeness, and redundancy.

Develop strategies for data security, backup, disaster recovery, business continuity, and archiving.

Required Skills & Experience

10+ years of experience in enterprise data modeling, including conceptual, logical, and physical model development.

Proven expertise in:

Data profiling, analysis, and optimization.

Canonical models, DaaS, SOA, and system integrations.

Master Data Management (MDM) tools and practices.

Metadata management and governance.

Enforcing data standards and reconciliation rules.

Capacity planning and lifecycle management.

Strong technical skills in SQL and experience with programming languages such as Python, R, or Scala.

Hands-on experience with Azure Synapse, SQL Server, Snowflake, and Databricks (or similar technologies).

Strong communication, presentation, and interpersonal skills; ability to convey technical concepts in business-friendly language.

Solid knowledge of data privacy practices and compliance requirements