Associate - Project Manager

Primary Objective:

Design and implement standardized, modular data models that can be adapted across multiple Operating Entities (OEs) at client, leveraging best practices from various modeling methodologies (Data Vault, Kimball, etc.) to deliver curated, reusable data products for business use cases.

Key Responsibilities

• Data Modeling Strategy - Develop and maintain enterprise-level data modeling standards based on Data Vault 2.0 principles. Harmonize methodologies (Data Vault, Kimball, Inmon) to create a unified modeling approach.


• Reusable Model Design - Create off-the-shelf data model templates that can be customized for different OEs. Ensure scalability and flexibility for diverse business domains.


• Data Product Development - Design curated datasets (data products) for specific analytical and operational use cases. Collaborate with business stakeholders to define requirements and ensure alignment with data governance.


• Architecture & Integration - Work closely with data engineers and solution architects to implement models in cloud/on-prem platforms. Ensure integration with existing data pipelines, ETL/ELT processes, and BI tools.


• Governance & Standards - Establish metadata management, lineage tracking, and documentation standards. Promote data quality, consistency, and compliance across all OEs.

Ideal Client Profile for this role:

• 9-12 years in data architecture and modeling roles using market leading data modeling tools like Erwin or others.
• Proven track record in designing enterprise data models and implementing Data Vault solutions.
• Strong communication skills to bridge technical and business teams.
• Bachelor s degree in computer science, Software Engineering, MIS or equivalent combination of education and experience

Required Skills:

• Expertise in Data Vault 2.0 methodology and tools.
• Strong knowledge of dimensional modeling (Kimball) and normalized approaches (Inmon).
• Experience with data warehousing, data Lakehouse architectures, and cloud platforms (AWS, Azure, GCP).
• Proficiency in SQL, ETL/ELT tools, and metadata management.
• Familiarity with data governance frameworks and data product concepts.