Data Modeler Lead Consultant
New
W
Weekday AIData warehousing
Remote (India), IndiaFull-TimeLead
Salary not disclosed
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Job Details
- Experience
- 8+ years
- Required Skills
- SQLErwinSnowflake
Requirements
- Bring 8+ years of experience.
- Have strong hands-on experience in 3NF modeling and relational database design.
- Demonstrate expertise in dimensional modeling, including star and snowflake schemas.
- Have a solid understanding of data warehousing concepts, architecture, and implementation.
- Be proficient in SQL, database structures, data relationships, and query optimization.
- Have experience translating complex business requirements into scalable enterprise data models.
- Bring strong communication, stakeholder management, analytical, and problem-solving skills.
- Experience with Snowflake and enterprise cloud data platforms is desirable.
- Hands-on knowledge of ERWin or ER/Studio is desirable.
- Familiarity with Databricks, Microsoft Fabric, SAP data environments, ETL/ELT, data integration, metadata management, or data governance is desirable.
Responsibilities
- Design, develop, and maintain conceptual, logical, and physical data models for enterprise applications, financial reporting, analytics, and data products.
- Apply 3NF modeling principles to create normalized relational data structures.
- Develop dimensional models, including star and snowflake schemas, facts, dimensions, and hierarchies.
- Design and optimize data warehouse structures for data integration, storage, transformation, and retrieval.
- Write, review, and optimize SQL queries to validate models, troubleshoot data issues, and improve query performance.
- Collaborate with business stakeholders to translate functional requirements, reporting needs, and data relationships into modeling solutions.
- Establish data modeling standards, naming conventions, documentation, metadata, and best practices.
- Work with data engineering and ETL/ELT teams to implement data models across enterprise data platforms.
- Evaluate data architectures and recommend improvements to scalability, performance, data quality, and maintainability.
- Support data governance, master data management, and data quality initiatives; lead design discussions and model reviews.
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