Sr Data Modeler (Data Warehousing & Governance)

Data Modeler

data modeling and data warehousing

Key Responsibilities

• Translate business requirements into long-term conceptual, logical, and physical data models.
• Design and govern data warehouse schemas, including fact and dimension models.
• Analyze and optimize existing data systems for consistency, quality, and performance.
• Collaborate with Data Engineering teams to align data models with ingestion and transformation pipelines.
• Define and maintain enterprise data modeling standards and best practices.
• Review schema changes and system enhancements to ensure cross-platform compatibility.
• Maintain, optimize, and govern metadata and local data models.
• Act as a data asset owner for assigned data domains, ensuring accountability and alignment with enterprise data strategy.
• Work closely with Product Owners, Data Engineers, Data Analysts, Architects, and business stakeholders to deliver data models aligned with the clients data vision.
• Support data quality initiatives by defining validation rules and resolving discrepancies.
• Prepare and maintain data model documentation, SOPs, and governance artifacts.
  

Technology & Environment

• Cloud Data Warehousing (AWS)
• Amazon Redshift
• AWS Glue Data Catalog
• Amazon S3
• EMR / Spark / Hive (metadata and schema understanding)
• Relational databases (RDS)
• SQL-based analytics platforms
  

What Were Looking For

• Bachelor’s degree in Computer Science, Statistics, or a related discipline.

• 10+ years of overall IT experience

  , with

  strong hands-on experience in data modeling and data warehousing

  .
• Proven expertise in conceptual, logical, and physical data modeling.
• Strong understanding of data warehouse design principles (fact/dimension modeling, SCDs, schema evolution).
• Working knowledge of AWS data services (Redshift, Glue, S3); deep infrastructure engineering experience is

  not required

  .
• Expert knowledge of metadata management and data governance practices.
• Experience acting as a

  Data Asset Owner

  or leading data domains.
• Advanced SQL proficiency for data validation, analysis, and reconciliation.
• Strong stakeholder management, leadership, and communication skills.
• Ability to operate effectively in a multi-stakeholder, multi-cultural enterprise environment.
• Data Modeling certifications (ERwin, CDMP, DAMA) are a plus.
  

Explicit Clarification (to Avoid ML Confusion)

does not require machine learning model development