Carolina Casualty is a member company of W. R. Berkley Corporation, an insurance holding company that is among the largest commercial lines insurance writers in the United States. We specialize in liability, physical damage, cargo and other insurance solutions for the commercial auto markets including trucking, public transportation and others.
Join Carolina Casualty, where innovation meets purpose.
Carolina Casualty is a proud member of W. R. Berkley Corporation, one of the largest commercial lines insurers in the country. What sets us apart is not just our size, but how we work and who we are.
We specialize in liability, physical damage, cargo, and other insurance solutions for the commercial auto market, including trucking and public transportation industries that keep America moving. Our team tackles real, complex challenges for the people and businesses that make that possible every day.
At CCIC, we live by three simple truths:
Always do right. Integrity is not just a slogan. It guides every decision we make.
Everything counts; everyone matters. Every role, every voice, and every contribution help shape who we are as an organization.
Embrace innovation and act responsibly. We are willing to challenge the status quo while remaining thoughtful and committed to our people, customers, and partners.
If you are looking for a place where your work makes an impact, your ideas are valued, and doing the right thing is expected, we would love to meet you.
The company is an equal opportunity employer.
We are seeking a Data Engineer with strong data modeling, governance and data quality capabilities to transform a fragmented data estate into a trusted enterprise foundation. This role will design ODS and DWH models, establish consistent data definitions and quality controls, and ensure that governance and SOX requirements are incorporated into both the current SQL Server environment and the future Databricks platform.
Candidate must sit in the Jacksonville, FL office.
Key Functions/Duties of Position:
· Analyze existing databases, marts and reporting structures to identify duplication and inconsistent models.
· Develop conceptual, logical and physical models for ODS and DWH solutions, including dimensional models.
· Define source-to-target mappings, business keys, historical data handling and transformation rules.
· Help establish data ownership, stewardship, metadata, lineage and critical data element standards.
· Define and automate data quality metrics for completeness, accuracy, validity, consistency, timeliness and reconciliation.
· Build and maintain SQL-based pipelines supporting governed ODS and DWH models.
· Support migration of models and pipelines to Databricks using SQL and, where appropriate, Python or PySpark.
· Document control points and evidence supporting traceable, SOX-compliant data processes.
Education Requirement
Bachelor’s degree in Computer Science, Information Systems, Data Analytics, Data Engineering, or a related field
Qualifications: Include necessary skills/experience and core competencies required
· Advanced SQL and Microsoft SQL Server experience.
· Strong relational and dimensional data modeling capabilities.
· Experience with ETL/ELT, ODS and enterprise data warehouse solutions.
· Experience defining data quality rules, reconciliations and source-to-target mappings.
· Ability to translate business concepts into scalable technical data models.
· Strong collaboration, documentation and problem-solving skills.
Preferred Qualifications
· Databricks, Python, PySpark or Spark experience.
· Experience implementing data governance, metadata, lineage or data catalog practices.
· Experience with automated data quality frameworks.
· Experience in a SOX-controlled or regulated environment.
· Property and casualty insurance industry knowledge, including policy, billing, claims or underwriting data, is a strong plus.
· Experience working with Guidewire applications or data, including Guidewire ClaimCenter (GWCC), Guidewire BillingCenter (GWBilling) or related policy systems.
· Experience designing governed, feature-ready data for predictive modeling or machine learning.
· Understanding of quality, lineage, privacy and control considerations for analytical models and AI solutions.
· Familiarity with generative AI, LLMs, RAG, vector data or unstructured data governance.
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