Policy Data Product Owner

Brown & Brown Insurance•Daytona Beach, FL

6d•$140,000 - $160,000

About The Position

Brown & Brown is seeking a Policy Data Product Owner to define, prioritize, and enable trusted enterprise Policy-domain data products within the gold layer of its medallion architecture. The role translates stakeholder needs across placement, account management, producers, finance, operations, carrier strategy, renewal management, and client analytics into a product vision, roadmap, prioritized backlog, acceptance criteria, quality standards, and adoption plan. These products support policy lifecycle intelligence, placement insights, retention analytics, carrier optimization, portfolio management, and self-service analytics. The Product Owner partners with business stakeholders, policy administration teams, engineering, BI, governance, and downstream consumers to ensure Policy-domain data products are valuable, usable, accurate, documented, secure, scalable, and aligned with enterprise standards. The position owns strategy, roadmap, backlog, stakeholder alignment, adoption, and value realization for capabilities covering policy detail and transactions, coverage, premium, revenue, carrier and intermediary relationships, placement, renewals, retention, insureds, producers, offices, lines of business, and reference data. The role is accountable for product outcomes rather than day-to-day engineering execution. It requires product ownership discipline, insurance policy-domain fluency, stakeholder management, prioritization, and the ability to connect policy lifecycle activity and related business measures to measurable value. The role is based in Daytona Beach, Florida. The stated annual pay range is $140,000–$160,000.

Requirements

At least 3 years of experience in data product ownership, product management, analytics product management, data analytics, policy administration analytics, placement analytics, insurance operations analytics, revenue analytics, or a related role; senior roles may require 5 or more years.
Experience managing a product roadmap, prioritized backlog, user stories, acceptance criteria, release planning, stakeholder communications, and adoption activities for data, analytics, or business intelligence products.
Strong understanding of analytical data platforms, SQL concepts, data modeling, data quality, reporting architecture, and the data product lifecycle.
Experience owning or enabling reporting datasets, analytical data structures, business-facing data products, dashboards, scorecards, or self-service analytics.
Experience with Policy-domain concepts such as policy details and transactions, coverage, effective and expiration dates, renewals, endorsements, cancellations, premium, revenue, commission, fees, carrier hierarchy, intermediary relationships, lines of business, product hierarchy, insured and client identifiers, producer attribution, office and profit-center mapping, and lifecycle reporting.
Understanding of data quality, documentation, testing, reconciliation, and requirements validation practices.
Ability to clarify stakeholder needs, make prioritization decisions, validate outcomes, explain tradeoffs, drive adoption, and ensure products deliver measurable business value.
Strong problem-solving and decision-making skills, including investigating data issues, assessing business impact, coordinating remediation, and balancing immediate stakeholder needs with long-term scalability.
Clear communication skills and ability to explain product direction, metric logic, roadmap priorities, release decisions, data limitations, and analytical findings to technical and non-technical audiences.

Nice To Haves

Bachelor’s degree in data science, computer science, engineering, information systems, analytics, statistics, business, or a related field.
Experience with AMS360, Epic, RiskMatch, BenefitPoint, BAM, Nova, policy administration systems, carrier and intermediary data, policy performance dashboards, placement insights, carrier scorecards, renewal reporting, or related insurance data products.
Experience with Microsoft Fabric, Azure, Databricks, Snowflake, Denodo, SQL-based platforms, or similar data engineering and analytics technologies.
Experience with data pipelines, data marts, transformation logic, medallion architecture patterns, data quality checks, or reporting-ready data structures.
Familiarity with DevOps, DataOps, version control, automated testing, deployment pipelines, and release management for data assets.
Familiarity with Agile, Scrum, Kanban, product discovery, delivery management, OKRs, value measurement, stakeholder governance, and product adoption practices.
Experience in insurance brokerage, policy administration, placement, carrier strategy, revenue operations, financial services, retail, or other enterprise-scale matrixed organizations where policy, coverage, premium, carrier, renewal, retention, and portfolio intelligence are core priorities.
Familiarity with placement strategy, preferred carrier utilization, wholesale versus direct placement, carrier concentration, renewal management, policy lifecycle analytics, coverage-gap identification, retention analytics, carrier scorecards, and adoption support for policy- and placement-facing analytical tools.

Responsibilities

Define and maintain the Policy data product vision, strategy, roadmap, outcomes, release priorities, and value measures in partnership with business and technical stakeholders.
Own the prioritized backlog for Policy-domain datasets and insight products integrating policy, transaction, coverage, premium, revenue, insured, producer, office, carrier, intermediary, line-of-business, and reference data.
Translate business problems, stakeholder needs, and reporting requirements into product features, user stories, acceptance criteria, metric definitions, release plans, and enablement materials.
Define and steward Policy-domain metrics and analytical capabilities, including policy counts, premium, revenue, commission, fees, policy terms, lifecycle events, placement outcomes, carrier concentration, intermediary use, and portfolio movement.
Partner with upstream data engineering teams to define source requirements, transformation rules, data quality expectations, business definitions, service-level needs, reconciliation methods, and dependencies.
Prioritize enhancement requests, defects, technical debt, data-quality issues, adoption needs, source-system gaps, and escalations based on business value, risk, feasibility, urgency, regulatory sensitivity, and outcomes.
Lead data validation and product acceptance, including policy-count reconciliation, premium and revenue tie-outs, lifecycle logic review, mapping validation, metric review, stakeholder signoff, defect triage, root-cause investigation, and release-readiness decisions.
Coordinate resolution of Policy-domain data quality issues and reporting discrepancies, including identifier inconsistencies, policy number variations, missing coverage attributes, date logic, line-of-business mappings, hierarchy gaps, and profit-center mapping issues.
Create and maintain product documentation, including requirements, user stories, metric definitions, data lineage summaries, decision logs, release notes, known limitations, adoption materials, and stakeholder signoff records.
Partner with BI, analytics, engineering, placement, finance, operations, and business teams to align dashboards, reports, analytical views, APIs, semantic models, and consumption experiences with standard definitions, user needs, priorities, access controls, and workflows.
Own adoption and continuous improvement of policy and placement enablement products, including policy performance and placement dashboards, carrier scorecards, Digital Bluebook views, recommendations, renewal intelligence, retention analytics, and coverage-gap analysis.
Lead release readiness, launch communications, stakeholder enablement, adoption tracking, data-quality feedback loops, and post-release improvement planning.
Ensure Policy-domain data products comply with enterprise standards for quality, security, traceability, privacy, access control, governance, retention, documentation, responsible use, and appropriate data handling.
Communicate product status, roadmap progress, risks, adoption results, metric changes, source limitations, data-quality findings, release impacts, and value realization to stakeholders and leadership.