Technical Project Manager – Retail Data & AI Products

Bridge Specialty Group•Atlanta, GA

1d•Hybrid

About The Position

Brown & Brown is seeking a Technical Project Manager – Retail Data & AI Products to join their growing team. This role will lead large-scale retail business data transformation initiatives, driving modernization of the data ecosystem using cloud and platform technologies. The position is responsible for end-to-end program ownership, aligning business outcomes with data strategy, delivering scalable data platforms, and enabling advanced analytics, AI, and machine learning use cases across insurance operations. The ideal candidate combines strong program leadership, data platform expertise, and executive stakeholder influence, with a focus on delivering measurable business impact.

Requirements

Bachelor’s degree in computer science, Engineering, Business, or related field
8+ years of program management experience in data, analytics, or enterprise technology
Proven track record delivering complex, cross-functional data programs at scale
Strong understanding of data platforms (data lakes, warehouses, pipelines)
Strong understanding of distributed systems and architecture principles
Experience with Agile / hybrid delivery models
Experience with program management tools (e.g., Azure DevOps)
Exceptional stakeholder management and communication skills
Strategic thinking with execution rigor
Cross-functional leadership and influence
Ability to translate ambiguity into structured execution plans
Executive communication and storytelling

Nice To Haves

Preferred Experience in Finance or Broking Insurance
Exposure to Azure, Databricks, Spark, Kafka, Snowflake
Exposure to AI/ML platforms and workflows
PMP or equivalent certification
Experience with data governance and compliance
Experience with cloud cost optimization (FinOps)
Experience with large-scale enterprise transformations

Responsibilities

Define and drive the enterprise data transformation vision and roadmap
Align data programs with business priorities
Establish governance frameworks for data platforms, AI/ML initiatives, and enterprise data standards
Translate strategic goals into execution-ready program plans
Own end-to-end delivery of large-scale data programs (data platforms, lakes, pipelines, AI initiatives)
Define scope, timelines, dependencies, and success criteria
Manage cross-team coordination (engineering, analytics, business)
Manage risks, issues, and escalation pathways
Ensure delivery aligns with scalability, reliability, performance, data quality, and governance standards
Maintain and evolve the enterprise data program roadmap
Align delivery to build vs. buy strategy and portfolio governance
Drive prioritization based on business value and ROI
Manage delivery tracking via Azure DevOps or similar platforms
Own program budgets, forecasts, and cost tracking
Ensure financial accountability and transparency
Apply FinOps principles for cloud cost optimization
Establish and report on program KPIs and value realization
Partner with senior business leaders to align outcomes and priorities
Deliver clear, concise executive-level communication and status reporting
Translate technical progress into business impact narratives
Influence cross-functional teams without direct authority
Collaborate with Data Engineering to define architecture, data pipelines, and platform roadmaps
Ensure alignment with modern data ecosystems (Azure, Snowflake, Databricks, ETL/ELT pipelines, streaming/batch architectures)
Drive adoption of scalable, reusable data platform capabilities
Define program-level testing strategies, UAT frameworks, and quality standards
Ensure adherence to data governance, privacy and compliance, and AI governance standards
Embed observability, monitoring, and data quality controls
Leverage AI tools (e.g., Copilot, AI-assisted development) to improve delivery efficiency
Support development of AI/ML-ready data platforms
Ensure responsible AI practices and governance compliance
Drive retrospectives and continuous improvement processes
Introduce scalable delivery frameworks, tools, and best practices
Improve operational efficiency across programs
Foster a culture of accountability, transparency, and innovation