Design and implement standardized data models, develop data modeling standards, and create reusable data model templates. Collaborate with business stakeholders and data engineers to implement models in cloud/on-prem platforms. Establish metadata management, lineage tracking, and documentation standards.
Key Highlights
Technical Skills Required
Benefits & Perks
Job Description
Role : Sr. Data Architect - Databricks
Domain Needed: P&C OR L&A insurance
Role Type: Full Time Permanent
Work Authorization: This role is not eligible for company sponsorship now or in future
Location: Remote- EST Time + Travel
Key Skills: Databricks, Azure, Data vault, Data modeling and commercial insurance knowledge; Able to guide jr. data modeler
Primary Objective - Design and implement standardized, modular data models that can be adapted across multiple Operating Entities (OEs) in 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.
Thought Leadership - Act as a subject matter expert for Data Vault 2.0 and modern data architecture. Mentor teams on best practices and emerging trends in data modeling.
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.