Data Analytics Engineer

Berkley•San Diego, CA

About The Position

Preferred Employers Insurance is seeking a Data Analytics Engineer to support the Actuarial department’s data, reporting, and analytical infrastructure. The role owns and enhances the actuarial sandbox datamart, data quality processes, reporting platforms, and analytical datasets used for actuarial, underwriting, claims, finance, and executive decision-making. The engineer partners with business and technical teams to deliver scalable, reliable, and well-governed data and analytical solutions and supports advanced analytics and predictive modeling initiatives. The position also coordinates day-to-day work and knowledge transfer from the department’s data engineering contractor, and establishes standards for data development, version control, testing, documentation, deployment, and maintenance. Preferred Employers Insurance provides workers’ compensation insurance to California businesses and serves small businesses, mid-to-large businesses, and group association members.

Requirements

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.
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.

Nice To Haves

Property and casualty insurance or workers’ compensation experience.
Experience coordinating contractors, vendors, or technical workstreams.
Experience with Databricks, cloud data platforms, or comparable data environments.

Responsibilities

Own, maintain, and enhance the actuarial sandbox datamart, including its 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 supporting 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.
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 for operational, actuarial, and executive reporting.