Director, Enterprise Analytics & Data Products

Raymond James•Saint Petersburg, FL

2d•Hybrid

About The Position

We are seeking a Director-level Data Products and Analytics leader to help the enterprise maximize the value of its data through trusted, reusable, AI-enabled data products, governed self-service analytics, and a scalable data marketplace. The leader will use AI as an enabler to improve discovery, consumption, automation, insight generation, and business value from data. They will shape the roadmap for enterprise analytics and AI-enabled data products while partnering with data engineering, analytics, governance, platform, and AI execution teams. A key priority will be defining the vision and execution path for an enterprise data marketplace that makes trusted data products easier to find, understand, request, access, and use responsibly across the organization. The ideal candidate brings strong data and analytics product leadership, financial services domain experience, practical AI fluency to drive outcomes, and the ability to align business demand with governed, scalable delivery.

Requirements

10+ years of experience in data, analytics, business intelligence, data products, self-service analytics, AI-enabled solutions, or related disciplines.
5+ years of leadership experience, preferably at Director level or equivalent.
Strong experience in wealth management, brokerage, advisory, asset management, or full-service financial services business with a strong understanding of the key data domains.
Proven experience leading data and analytics product capabilities such as data products, enterprise BI, self-service analytics, semantic layers, reporting platforms, or analytics modernization.
Must have experience with semantic layers, including ontology-driven semantic models, business-friendly metrics and definitions, and knowledge graph concepts that connect data, relationships, and business context for analytics and AI-enabled consumption.
Experience shaping or operating a data marketplace, data catalog, data product catalog, governed access model, or enterprise data consumption strategy.
Practical AI fluency, including how AI can improve data discovery, analytics, automation, requirements development, and business decision support.
Experience leveraging AI to maximize business value from enterprise data investments, including improving data discovery, consumption, insight generation, productivity, and adoption of data products.
Strong understanding of enterprise data platforms, data models, data quality, metadata, lineage, governance, access controls, and data consumption patterns.
Demonstrated ability to translate business needs into actionable requirements for data engineering, analytics, reporting, platform, governance, and AI delivery teams.
Strong stakeholder management skills with the ability to influence business and technology teams.
Ability to operate from vision and roadmap through detailed execution, including getting hands-on as needed to analyze and profile data, clarify requirements, identify solution options, and convert business needs into actionable work for technology teams.
Bachelor’s: Business Administration, Bachelor’s: Computer and Information Science, Bachelor’s (Required)

Nice To Haves

Wealth management experience: Experience in a full-service wealth management, brokerage, advisory, or asset management business, with familiarity across advisor, client, household, account, portfolio, product, transaction, performance, supervision, risk, and service data domains.
Enterprise data platforms experience: Working knowledge of modern enterprise data platforms and patterns such as Snowflake, Redshift, Data Bricks, Data Lakehouse’s, semantic layers with ontology, APIs, metadata, lineage, data quality, and governed data product architectures.
Analytics tooling experience: Experience shaping analytics capability roadmaps across tools such as Qlik, Tableau, ThoughtSpot, AWS Sagemaker, AWS Bedrock, Alteryx, Excel-based analytics, governed reporting, dashboards, KPI frameworks, metrics layers, semantic models, and self-service analytics platforms.
AI-enabled data products and marketplace experience: Experience defining or maturing AI-enabled data products, data product catalogs, data marketplaces, governed access models, product onboarding, metadata, business glossaries, certification, access workflows, usage transparency, adoption, and value measurement.
AI fluency: Practical understanding of how AI, machine learning, generative AI, natural language analytics, intelligent search, AI-assisted insight generation, summarization, anomaly detection, predictive analytics, and automation can increase the value and usability of trusted enterprise data.
Hands-on analysis and profiling: Ability to review data models, source tables, reports, dashboards, joins, keys, grain, relationships, business rules, data quality issues, usage patterns, and source-to-consumption gaps to validate assumptions and shape solution options.
Requirements-to-solution translation: Ability to convert business needs into actionable technology deliverables, including use cases, user stories, data requirements, analytics requirements, access requirements, controls, acceptance criteria, dependencies, testing considerations, and implementation sequencing.
Transformation and execution leadership: Experience leading analytics modernization, self-service BI transformation, data product strategy, data marketplace enablement, platform adoption, operating model change, stakeholder engagement, executive communication, adoption metrics, and measurable business value realization.

Responsibilities

Own the product vision, roadmap, prioritization, and execution alignment for enterprise analytics, AI-enabled data products, governed self-service analytics, and the enterprise data marketplace.
Define and drive the data marketplace strategy, including product cataloging, metadata, discoverability, access request patterns, usage transparency, adoption, and business value measurement.
Define and manage the analytics tool capabilities roadmap, ensuring platforms, tools, and user experiences evolve to make data and insights easier to discover, access, analyze, and consume.
Mature analytics capabilities and operating practices to promote governed, intuitive, self-service access to trusted data, insights, dashboards, analytics products, and AI-enabled decision support.
Partner with business stakeholders to understand decision-making needs, data consumption patterns, pain points, and opportunities to create reusable data products.
Translate business needs into clear, actionable requirements for data engineering, analytics, reporting, platform, governance, and AI execution teams.
Leverage AI to improve data discovery, insight generation, productivity, analytics workflows, documentation, and user experiences.
Work with AI, data science, and technology teams to shape practical AI-enabled capabilities built on trusted enterprise data.
Promote governed self-service access by ensuring data products are trusted, well-defined, secure, discoverable, and easy to consume.
Partner across business and technology teams to ensure data, analytics, AI-enabled capabilities, and marketplace access patterns are responsibly designed, appropriately controlled, and aligned to enterprise standards.
Define success metrics for data products, marketplace adoption, analytics consumption, quality, trust, reuse, business value, and stakeholder satisfaction.
Build trusted relationships with senior business and technology stakeholders and influence priorities across teams.