- QUALIFICATION STANDARDS
- Educational Attainment:
- Bachelor's degree in Computer Science, Information Systems, or a related field or equivalent hands-on experience.
- Professional Experience & Leadership
- 7+ years of hands-on experience in data engineering, with at least 2 years in a leadership role.
- Proven experience leading, building, and mentoring data teams in a small to mid-sized organization.
- Extensive experience overseeing data warehouse projects from inception to completion.
- Technical Skills
- Experience with Microsoft Azure data services is required.
- Strong SQL and Python skills; experience with Power BI and Medallion Lakehouse architecture is a plus.
- Deep understanding of data warehouse schemas (fact/dimension tables), ETL pipelines, and data modeling.
- Strong knowledge of data architecture, ETL processes, and data governance.
- Familiarity with Microsoft Fabric and Databricks platforms is highly desirable.
- Experience in the Commercial Insurance P&C domain is a must.
- Excellent communication, leadership, and project management skills.
- Demonstrated success managing diverse teams (onshore/offshore, consultants, FTEs).
- Strong analytical and problem-solving skills. Critical thinking and problem-solving is a must.
- Preferred Skills:
- Experience supporting pricing, actuarial, underwriting, and financial reporting use cases within a Commercial P&C environment
- Proven experience modernizing legacy reporting and data processes, including replacing or replatforming vendor-managed or manual reporting solutions into enterprise data platforms
- Experience identifying and resolving data quality issues, inconsistencies, and gaps in legacy data processes, improving trust in reporting outputs
- Demonstrated ability to balance evolving business requirements with structured data design, avoiding both over-engineered solutions and ad hoc rework
- Experience designing and implementing governed self-service data models and semantic layers, enabling business-facing analytics and reporting (e.g., Power BI and DaaS models)
- Familiarity with external reporting and data exchange patterns, including structured ingestion and delivery of data feeds for partners, carriers, and regulatory requirements
- Experience designing and extending modern data platforms (e.g., Microsoft Fabric) beyond traditional medallion layers, including semantic model development, data governance (e.g., Purview), and structuring data to enable downstream analytics, applications, and AI use cases
Experience enabling or supporting AI and advanced analytics workflows, including preparing and structuring governed data for consumption by applications, integrations, and AI-driven use cases
