Hybrid - 3 days in the office - Lisbone
Job Summary:
The Production Support Data Analyst is a hands-on role focused on ensuring the accuracy and completeness of data within the enterprise data warehouse. This position will collaborate with the IT team to support and maintain the enterprise data warehouse architecture at Sompo International. The data warehouse plays a critical role in measuring operational performance across the organization.
- Work with business stakeholders and IT teams to analyze and resolve data issues within the enterprise data warehouse.
- Develop source-to-target mapping documentation to incorporate additional application data elements.
- Load source-to-target mapping documents from source systems into the enterprise data warehouse.
- Collaborate with the ETL team to ensure that source-to-target mappings align with ETL code requirements.
- Validate ETL code compliance with STM requirements.
- Perform data validation to ensure accurate data movement within the enterprise data warehouse architecture.
- Work with reporting teams to analyze and explain discrepancies between the enterprise data warehouse and source system applications.
- 5+ years of experience in data analysis within an enterprise data warehouse environment.
- At least 2 years of subject-matter expertise in Policy & Claims Insurance.
- Advanced proficiency in SQL.
- Strong understanding of Data Warehousing, ETL, and BI architectures.
- Experience in creating and optimizing semantic layer reporting views.
- Ability to lead data discovery sessions with business subject-matter experts.
- Familiarity with RDBMS platforms (e.g., SQL Server, DB2) and experience generating DDL.
- Knowledge of data modeling concepts.
- Experience with Guidewire Policy Center, Guidewire Claims Center, SAP FS-RI, and SAP FS-CD is a plus.
- Excellent interpersonal skills, including strong written and verbal communication.
- Ability to work effectively as part of a team.
- Strong organizational and analytical skills.
- Bachelor’s degree in Computer Information Science, Information Management, or Statistics.