Berkley
Data Analytics Engineer
1615 Murray Canyon Road, San Diego, CA 92108
On-site · $117k–$139k (as posted)
First seen by offpeak October 23, 2026
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Company Details
Preferred Employers Insurance, A Berkley Company specializes in providing workers’ compensation insurance to California business owners. The company serves three major Product/Client Segments: Small Business, Mid-Larger Businesses and Group Association Members (Programs). The company’s distribution partners (agents & brokers) number just under 400 locations throughout the state. Preferred serves thousands of policyholders and provides medical claims handling and claims management as needed to care for injured workers. The company is rated A+ Superior by industry-rating organization, AM Best & Company.
Company URL: https://www.peiwc.com
The company is an equal opportunity employer.
Responsibilities
Preferred Employers Insurance is seeking a Data Analytics Engineer to support the Actuarial department's data, reporting, and analytical infrastructure. This role will own and enhance the actuarial sandbox datamart, data quality processes, reporting platforms, and analytical datasets that support actuarial, underwriting, claims, finance, and executive decision-making. The Data Analytics Engineer will partner with business and technical teams to deliver scalable, reliable, and well-governed data and analytical solutions while supporting advanced analytics and predictive modeling initiatives.
Key functions include but are not limited to:
- Own, maintain, and enhance the actuarial sandbox datamart, including data architecture, permissions, documentation, and integration of new data sources.
- Serve as the Actuarial department’s primary liaison for enterprise data platform initiatives, including requirements gathering, testing, issue resolution, implementation, and adoption.
- Lead data stewardship activities, including data validation, reconciliation, QA/QC, auditing, root-cause analysis, and remediation of data integrity issues.
- Automate data pipelines, reporting workflows, and repeatable analytical processes using SQL, Python, R, Power BI, Databricks, or comparable tools.
- Perform business analytics that support underwriting, claims, finance, distribution, and product decisions.
- Partner with actuarial staff on predictive modeling, segmentation, KPI development, model monitoring, and implementation of analytical solutions.
- Translate business questions into clear data requirements, analytical approaches, and practical recommendations for technical and non-technical audiences.
- Coordinate day-to-day work and knowledge transfer from the department’s data engineering contractor, including documentation of systems and processes.
- Establish scalable standards for data development, version control, testing, documentation, deployment, and ongoing maintenance.
- Manage multiple recurring and ad hoc priorities while communicating status, risks, dependencies, and recommended actions.
- Proactively identify opportunities to improve data quality, reporting efficiency, analytical capability, and business decision support.
- Design, develop, and maintain curated analytical datasets, Power BI semantic models, dashboards, recurring reports, and monitoring tools that serve as trusted sources for operational, actuarial, and executive reporting.
Qualifications
- Bachelor’s degree in data science, computer science, statistics, mathematics, actuarial science, engineering, or a related quantitative field.
- 3–5 years of relevant experience in analytics engineering, data engineering, business intelligence, or advanced analytics.
- Property and casualty insurance or workers’ compensation experience is preferred.
- Advanced proficiency in SQL and working proficiency in Python or R.
- Experience building and maintaining automated data pipelines, analytical datasets, and reporting solutions.
- Experience with Power BI, semantic models, dashboard development, and business intelligence solutions.
- Demonstrated ability to lead analytics, reporting, automation, or data engineering projects.
- Understanding of data quality, reconciliation, testing, and documentation practices.
- Strong written and verbal communication skills with technical and non-technical audiences.
- Ability to collaborate across departments and translate business needs into scalable data and analytical solutions.
- Experience coordinating contractors, vendors, or technical workstreams is preferred.
- Experience with Databricks, cloud data platforms, or comparable data environments.