Senior Data Architect with experience and track record with successful delivery of Guidewire Claim Center migration and Data conversion to Cloud

Start Date: June 16, 2025 to December 31, 2025

Term: 6.5 Months

Extension option(s): 6 Months

Hybrid (once a month)

1. 10+ years experience in Design and Data Modelling.
2. 7+ years experience in ETL, Data Migration, Data analysis and Data lineage specific activities (e.g. Azure ADF, DataStage, SAPBODS, AWS migration service).
3. 5 + years of Strong experience in BI, Reporting and data analytics
4. Expertise in database, data warehousing technologies and tools (e.g., SQL Server, Oracle, Azure Datalake, Azure Synapse, Parquet, Cognos)
5. Experience with Cloud migration from On-Prem to Cloud
6. 5+ Years of experience working with Guidewire Insurance Suite with ClaimCenter module, including reporting and analytics
7. 2+ years experience working with Guidewire data, CDA (Cloud Data Access)
8. 5+ years senior level communications experience including document prep, conducting workshops

Resource Responsibilities:

Data Strategy Development:

• Collaborate with business leaders to identify data needs and develop a comprehensive data architecture roadmap aligned with strategic goals.
• Analyze current data landscape and identify areas for improvement, including data quality, accessibility, and integration.
• Define data standards, governance policies, and data quality metrics.
• Define Data Migration strategies that will help realize roadmap.

Data Modeling and Design:

• Design and implement data models for various data sources, including operational systems, claims data, and external data feeds.
• Create logical and physical data models to ensure efficient data storage and retrieval.
• Develop data warehouse and data lake architectures to support analytics and reporting needs.

Data Integration and ETL:

• Design and manage data pipelines to extract, transform, and load (ETL) data from disparate sources into the data warehouse.
• Ensure data consistency and accuracy across different systems.

Data Security and Compliance:

• Implement robust data security measures to protect sensitive stakeholder information and comply with privacy regulations.
• Monitor data access controls and audit data usage.

Performance Optimization:

• Monitor data performance and identify bottlenecks to optimize data processing speeds and query efficiency.
• Implement data caching strategies to improve response times.

Stakeholder Management:

• Collaborate with business users, IT teams, and data analysts to understand data requirements and deliver solutions that meet their needs.

Communicate data architecture concepts and plans effectively to stakeholders.