SUPERVISOR, DATA MODELING

Purpose of the job

A Data Modeler will support the creation of Logical and Physical Data Models using normalized (3NF) and dimensional structures. This role is enabling Enterprise Data Analytics and decision-making by adhering to architectural standards, governance best practices, and ensuring data consistency. Collaboration with business stakeholders to define and document business requirements while promoting data modeling best practices across the organization.

Duties and responsibilities

1. Develop Logical and Physical data models for OLAP and OLTP systems.
2. Enforce modeling standards including normalization, denormalization, data transformation, data lineage, and data quality for the Enterprise Data Platform.
3. Transform raw data from multiple sources into actionable insights.
4. Conduct modeling sessions with project teams across business units to gather and define data requirements for the Enterprise Data Models.
5. Collaborate with business and data teams to enhance data models quality and validity for decision support entities.
6. Perform light data analysis and profiling using SQL queries on metadata as needed.
7. Document data models for building reference that includes definitions for business terms and KPIs.
8. Conceptualize complex data schemes while ensuring alignment with modern data architectures like Data Lakehouse.
9. Collaborate with business stakeholders for requirements gathering and ensure alignment of data models with business needs.

Job specification

Education

Bachelor's degree from a recognized university in Computer Science, Engineering, or any relevant field.

Experience

1. 3 - 5 Years Experience In data modeling, statistical analysis, data manipulation, and ETL.
2. Telecom industry experience is a strong plus

Skills and abilities

• Strong proficiency of English language spoken and written
• Advanced level in using SQL for analytics.
• Knowledgeable in Inmon and Kimball approaches for designing Data Warehouse and Data Mart architectures.
• Familiar with using Data Build Tool (DBT) and Python.
• Knowledge of big data ecosystems and trending data platform architectures.
• Strong analytical mindset with experience in turning business requirements into logical and physical models.
• Ability to grasp concepts quickly.