Data & Analytics Solutions Consultant

Analytics Technology Solutions Lead — Commercial Insurance

A unique opportunity to join a specialization-driven, client-focused commercial insurance brokerage to develop and deploy analytics applications of the future!

We are looking for an Analytics Technology Solutions Lead (Data & Analytics Solutions Consultant) who builds things that influence insurance deal cycles. This is not a mere data engineering or infrastructure role. You will design, build, and deploy production-grade analytics applications that support placement decisions, benchmark limits and pricing, and give clients a clearer view of their risk across Commercial Lines and Employee Benefits.

Your work spans the full delivery cycle: translating broker and BA requirements into technical specifications, architecting data pipelines, building Python Dash applications and maintaining the critical data & analytics infrastructure. By operationalizing intelligent dashboards and actuarial models, you create best self-service experience and provide client-ready deliverables accelerating operational efficiency and increasing sales velocity for the enterprise.

If you are a hands‑on builder with creative imagination, comfortable leveraging modern development tools including AI‑assisted coding environments to produce production grade codes, love troubleshooting and solving problems, and understand actuarial intricacies of the business
, you will thrive in this role!

Hybrid - at least 3 days a week in one of our EPIC offices, preferably San Ramon CA but open to any of our office locations.

• Design and lead full-stack development of analytics applications using Python, Dash, and Fast API hosted on Databricks and Azure from architecture through production.
• Build broker‑facing UIs that are intuitive and produce client‑ready deliverables with limited analyst intervention.
• Operationalize statistical and actuarial models for loss trend analysis, frequency‑severity decomposition, and program pricing validation in collaboration with actuaries, data scientists, and claims analysts.
• Develop and maintain data pipelines and adapters that ingest, normalize, and quality‑check data from clients, carriers and third‑party sources.
• Apply CI/CD practices across both pipeline and application deployments and maintain governance and auditability standards appropriate for client‑facing work.
• Collaborate with data engineering partners on scalable architecture that supports growing benchmarking and modeling workloads.
• Partner with Business Analysts to translate brokerage workflows into application requirements and data architecture decisions.
• Engage directly with brokers and client executives to understand value of analytics in deal cycles and develop modular architecture for variety of solutions.
• Working with change management leads, create adoption plans, training materials, and feedback mechanisms for every tool shipped.

• Python/Dash:
  Expert‑level proficiency for application development, data pipelines, statistical modeling, API development, and application logic — this is your primary tool.
• Database:
  Deep SQL expertise with relational and columnar environments; comfort with large underwriting, loss and related data domains.
• Actuarial & Data Science:
  Working knowledge of building, evaluating, and deploying actuarial analysis and models.
• Cloud:
  Hands‑on experience with Databricks, Azure, or equivalent cloud data platforms for deploying and managing CI/CD pipelines.

• 7+ years in the insurance industry (or equivalent experience in a data‑intensive financial services environment) in either combination of brokerage, carrier, or consulting with strong focus on delivering data & analytics solutions in a process driven environment.
• Bachelor's degree in computer science or any quantitative engineering;
  Master's degree is preferred.

• Working knowledge of commercial lines insurance brokerage workflows and actuarial concepts: underwriting applications, submissions, program towers, loss runs, exposure schedules, coverage terms, renewals, RFP cycles, loss development factors, IBNR, frequency‑severity, layer…