Data Modeler

We are seeking an experienced Data Modeler to design and implement high-quality analytical data models that support enterprise reporting, analytics, and data science use cases. The ideal candidate will have hands-on expertise in Silver and Gold layer modeling, dimensional data warehouse design, and Python automation to streamline data modeling, validation, and documentation processes.

Required Skills and Qualifications:
8–12+ years of experience in data modeling and data warehousing.
Strong hands-on experience with Silver & Gold layer modeling
Dimensional modeling (Star / Snowflake schemas) and Data warehouse design
Python automation. Deep understanding of fact/dimension design, grain definition, and KPI modeling.
Experience with large-scale analytical datasets.

Good to Have:
Microsoft Certified: Azure Data Engineer Associate or Azure Enterprise Data Analyst Associate.
Experience with cloud data platforms (Azure).Exposure to CI/CD and version control for data models.
Knowledge of metadata management and data governance tools.
Wast management or oil and gas domain knowledge

Key Responsibilities:
Ø Experience with database modeling tools such as LucidChart"
Ø Experience with standard data models such as S-95
Ø Design and develop Silver and Gold layer data models following medallion architecture principles.
Ø Create and maintain dimensional models (Star and Snowflake schemas) for enterprise data warehouses.
Ø Define fact and dimension tables aligned with business KPIs and analytical requirements.
Ø Apply best practices for slowly changing dimensions (SCDs) and historical data tracking.
Ø Lead end-to-end data warehouse design and modeling initiatives.
Ø Translate business requirements into scalable, performant data models.
Ø Optimize models for query performance, usability, and extensibility.
Ø Partner with data engineers to ensure accurate implementation of models.
Ø Develop Python-based automation for data model validation, reconciliation and documentation.
Ø Automate metadata extraction, schema comparison, and impact analysis.
Ø Support automated testing of data models and transformations.
Ø Build reusable Python utilities to improve modeling efficiency.
Ø Collaborate with data engineers, analysts and architects.
Ø Participate in design reviews and ensure alignment with enterprise data standards.
Ø Support data governance initiatives including data definitions, lineage, and documentation.
Ø Ensure consistency in naming conventions, data types, and modeling standards.