Head of AI and Data Platform Engineering - Specialty Distribution

Brown & Brown Insurance•Daytona Beach, FL

3d•Hybrid

About The Position

Bridge Specialty Group is seeking a Head of AI & Data Platform Engineering – Specialty Distribution to join our growing team in Plano, TX or Daytona Beach, FL! The Head of AI and Data Platforms is responsible for leading the Specialty Distribution Data and AI Engineering function and defining the strategy, operating model, and execution roadmap for enterprise-grade data and AI products. This role enables the business to consume trusted data, analytics, reporting, insights, automation, and AI solutions that improve decision-making, operational performance, customer experience, and growth. This leader will oversee the design, delivery, governance, and ongoing operation of scalable data platforms, data products, AI solutions, and engineering practices. The role requires a strategic technology leader who can partner with enterprise technology, business leadership, security, analytics, and engineering teams while also driving modern software engineering, DevOps, cloud, and data platform practices across the organization.

Requirements

Bachelor’s degree in business, data science, computer science, information systems, engineering, or a related field.
10+ years of progressive experience in data engineering, AI, analytics platforms, software engineering, technology leadership, or related roles.
Proven experience working with Director, VP, and C-suite stakeholders in a large-scale, matrixed enterprise environment.
Demonstrated success leading data, AI, engineering, or platform teams at scale, including people leadership, operating model design, and delivery accountability.
Experience managing large technology budgets, vendor relationships, delivery portfolios, and enterprise technology roadmaps.
Deep experience delivering data and AI solutions at scale with measurable business value.
Strong technical understanding of modern data platforms and cloud ecosystems, including Azure, Databricks, Data Lake, Azure Data Factory, Synapse, SQL, Power BI, Denodo, Snowflake, Cosmos DB, Boomi, Azure AI Foundry, and related technologies.
Working knowledge of Python, SQL, Spark, data orchestration, data integration, data modeling, data governance, and data platform operations.
Experience implementing modern SDLC practices, including CI/CD, automated testing, branching strategies, Infrastructure as Code, environment management, and DevSecOps practices.
Strong problem-solving skills with the ability to navigate ambiguity, identify root causes across technical and organizational layers, and drive practical solutions.
Excellent leadership, communication, stakeholder management, and executive presentation skills.

Nice To Haves

Master’s degree in business, data science, computer science, information systems, engineering, or a related field.
Experience in the insurance industry, including awareness of risk, regulatory, compliance, privacy, and data protection considerations.
Experience securing data across multi-national environments with geo-specific regulatory requirements such as GDPR, LGPD, or similar frameworks.
Hands-on familiarity with Terraform, Bicep, Azure DevOps, Key Vault, Infrastructure as Code, and automated deployment practices.
Experience mentoring or coaching teams on engineering excellence, data engineering standards, DevSecOps, platform reliability, and AI enablement.
Software engineering background in Java, .NET, or similar enterprise application development technologies.

Responsibilities

Own and evolve the Specialty Distribution Data strategy while contributing to broader Enterprise Data, AI, and Specialty Distribution AI strategies.
Develop and maintain roadmaps that translate business priorities into executable data, analytics, and AI platform initiatives.
Establish scalable architecture patterns, engineering standards, governance practices, and delivery frameworks for data and AI solutions.
Implement extensible and reusable data products that align with enterprise models, technology standards, security controls, and platform patterns.
Manage data and AI risk, including compliance with enterprise data, AI, security, privacy, and regulatory policies.
Partner with enterprise architecture, security, infrastructure, software engineering, analytics, and business leadership teams to ensure solutions are secure, scalable, reliable, and aligned to enterprise strategy.
Build and lead a best-practice data and AI engineering operating model, including team structure, processes, tools, standards, governance, and ways of working.
Ensure the reliable operation of data services, platforms, pipelines, integration services, orchestration tools, reporting data sets, and supporting infrastructure.
Oversee the design, build, optimization, and support of modern data pipelines using technologies such as Databricks, Delta Lake, Azure Data Services, Azure Data Factory, Azure Data Lake, Synapse, SQL, and related platforms.
Establish high-quality engineering practices for modular code, reusable components, automated deployments, environment management, branching strategies, CI/CD pipelines, and SDLC discipline.
Drive continuous improvement across data engineering, DevOps, data operations, platform reliability, and delivery processes.
Support data and BI developers by operationalizing analytics, reporting workflows, curated datasets, semantic layers, and visualization-ready data products.
Lead the delivery of AI solutions, AI agents, automation capabilities, and intelligent products in partnership with Enterprise AI, business teams, and platform stakeholders.
Deploy incubated AI solutions into production and scale successful capabilities across the business to expand adoption, value realization, and operational coverage.
Ensure AI products are designed with appropriate governance, security, compliance, monitoring, responsible AI practices, and measurable business outcomes.
Partner with Enterprise AI and Communications teams to support training, enablement, and adoption of AI tools such as Microsoft Copilot and other approved enterprise AI platforms.
Deliver data and AI initiatives from concept through production, ensuring solutions are aligned to business priorities, enterprise standards, and measurable value.
Build strong relationships with executive stakeholders, business leaders, product owners, analytics teams, and technology partners to translate business needs into effective data and AI capabilities.
Manage and prioritize a backlog of enhancements, small changes, platform improvements, and new data product needs.
Support onboarding and integration of acquisitions into the company’s data, analytics, AI, and systems landscape.
Identify inefficiencies across technical pipelines, platform operations, and engineering processes, and design pragmatic solutions that improve speed, quality, reliability, and scalability.
Build, lead, and develop a high-performing AI and data engineering organization capable of delivering at enterprise scale.
Provide clear direction, coaching, performance management, and professional development for leaders, engineers, architects, and platform specialists.
Foster a culture of accountability, collaboration, innovation, engineering excellence, security awareness, and continuous improvement.
Champion modern data engineering, DevSecOps, cloud, and responsible AI practices across the organization.
Communicate effectively with senior executives, technical teams, business stakeholders, and cross-functional partners, translating complex technology concepts into clear business value.