Senior Data Modeler (Azure)

Role & responsibilities

Job Summary

Data Modeler

Key Responsibilities

Data Model Design & Development

• Conceptual, Logical, and Physical Modeling:

  Design, develop, and maintain conceptual, logical (3NF), and physical data models (Dimensional/Star Schema) for both operational and analytical systems.

• SQL Server & Databricks Integration:

  Develop and maintain data models within

  SQL Server

  for OLTP/OLAP systems and implement modern

  Medallion Architectures

  (Bronze, Silver, Gold layers) using

  Delta Lake

  tables within

  Azure Databricks

  .

• Dimensional Modeling:

  Utilize and enforce

  Dimensional Modeling

  techniques (e.g., Star Schema, Snowflake Schema) to optimize data structures for high-performance reporting and analytics consumption (e.g., Power BI/Tableau).

Architecture & Governance

• Requirements Translation:

  Collaborate with business users, Data Architects, and Data Engineers to gather data requirements and translate them into technical design specifications.

• Data Governance:

  Define and enforce data governance standards, data dictionary, naming conventions, and data quality rules across all modeled objects, leveraging

  Databricks Unity Catalog

  for centralized metadata management where applicable.

• Optimization:

  Perform SQL query tuning and data model optimization across both platforms to improve query performance, reduce latency, and minimize data storage costs.

Collaboration & Documentation

• Cross-Functional Partnership:

  Work closely with ETL/Data Engineers to design and validate data pipelines (using tools like Azure Data Factory or Databricks notebooks) to ensure efficient data flow and accurate mapping to the target models.

• Documentation:

  Create and maintain comprehensive documentation, including data dictionaries, data lineage maps, and entity-relationship diagrams (ERDs) using tools like

  ERwin

  or

  ER/Studio

  .

Required Qualifications

Technical Expertise

• Data Modeling Tools:

  5+ years of hands-on experience in data modeling, database design, and development, including proficiency with industry-standard modeling tools (e.g., ERwin, PowerDesigner, or similar).

• SQL Proficiency:

  Expert-level proficiency in

  SQL

  (T-SQL or standard SQL) with the ability to write, review, and optimize complex queries, stored procedures, and schema objects in

  SQL Server

  .

• Azure Databricks:

  Strong hands-on experience designing and implementing data models and structures using

  Azure Databricks

  and

  Delta Lake

  (e.g., Medallion architecture).

• Data Warehousing:

  Deep understanding of data warehousing concepts, including Kimball Dimensional Modeling (Facts and Dimensions) and Inmon 3NF principles.

• Cloud Services:

  Working knowledge of the

  Azure Cloud Ecosystem

  , particularly Azure Data Lake Storage (ADLS Gen2) and how Databricks integrates with other Azure services. Education & Soft Skills

• Education:

  Bachelor's degree in Computer Science, Information Technology, or a related quantitative field.

• Communication:

  Excellent verbal and written communication skills with the ability to explain complex data concepts to technical and non-technical stakeholders.

• Problem-Solving:

  Proven analytical and problem-solving skills, with a keen eye for detail and data quality.

Preferred Qualifications (Nice-to-Have)

• Experience with

  PySpark

  or

  Scala

  for large-scale data transformation logic within Databricks notebooks.
• Familiarity with other Azure data services (e.g., Azure Synapse Analytics, Azure Data Factory).
• Experience in a regulated industry (e.g., Finance, Healthcare).
• Certification such as

  Databricks Certified Data Engineer

  or

  Microsoft Certified: Azure Data Engineer Associate

  .