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Tampa, Florida, United States
Onsite
The Baldwin Group is guided by the Azimuth, our cultural constitution and True North, aligning our mission, vision, values, and promises to colleagues and clients alike. The Head of Data & Analytics owns the build-out and ongoing operation of Baldwin's enterprise data foundation- the governed, AI-ready lakehouse, the enterprise ontology and knowledge graph, and the analytics capability that the firm's growth, operating, and AI ambitions all depend on.
This is a builder's role. This leader is accountable for delivery: onboarding priority data domains into the Databricks lakehouse, modeling Baldwin's core business entities and relationships into a usable knowledge graph, standing up the governance and controls that make our data trustworthy in a regulated industry, and putting accurate, timely insight in the hands of the colleagues who run our businesses. Reporting to the Chief Technology Officer, this leader executes within the enterprise data, AI, and architecture direction set by the CTO organization, and partners closely with the AI Role Transformation team, segment and Growth Services leaders, and enterprise architecture to ensure Baldwin's data is trusted, accessible, and ready to power both traditional analytics and Agentic AI — all in service of Baldwin Alchemy, achieving more together than any of us could alone.
We are looking for a leader who has done this before at enterprise scale: someone equally comfortable in a Databricks notebook, in a data modeling session, in a governance forum, and in front of a business unit president explaining what the numbers mean.
· Own end-to-end execution of Baldwin's Databricks lakehouse strategy, including the medallion architecture (bronze/silver/gold), ingestion pipelines, and the entity prioritization roadmap.
· Onboard priority data domains - policy, client, carrier, commission, colleague, and financial into the lakehouse on a sequenced, business-value basis, in coordination with segment and Growth Services stakeholders.
· Build and maintain the platform engineering practices - CI/CD, automated testing, observability, incident response, and documented SLAs — needed to run the lakehouse as production infrastructure, not a series of one-off pipelines.
· Rationalize the existing estate: consolidate duplicate pipelines and reporting extracts, retire legacy data stores, and reduce the number of places a given number can come from.
· Manage platform cost, performance, and reliability, reporting transparently on lakehouse health, consumption forecasting, and roadmap progress.
· Own the enterprise data ontology, the canonical definitions of Baldwin's core business entities and the relationships among them so that client, policy, carrier, producer, coverage, and claim mean one thing across every system and every consuming application.
· Lead the build-out of Baldwin's knowledge graph: model the entities and relationships that describe our business, populate the graph from governed lakehouse data, and maintain it as a living asset rather than a one-time modeling exercise.
· Champion a unified semantic layer and governed metrics on top of the ontology, so a business definition holds whether it powers a dashboard, a carrier submission, or an AI agent's answer.
· Make the graph consumable, expose it through APIs and retrieval patterns such as graph-based RAG, so AI agents and analysts can reason over relationships, hierarchies, and provenance rather than retrieving isolated records.
· Resolve identity across the estate: entity resolution and mastering for clients, producers, and carriers, so the graph reflects one real-world entity rather than five system records.
· Sequence ontology and graph work pragmatically, model the domains that unlock prioritized analytics and AI use cases first, and resist modeling for its own sake.
· Own data governance, quality, ownership, and lineage across core data domains, including the practical controls - access management, automated data quality checks, auditability that keep the platform trustworthy.
· Implement the controls and evidence practices needed to satisfy internal audit, client and carrier data obligations, privacy law, and regulatory inquiry and represent the data function in those reviews.
· Partner with Legal, Compliance, and Information Security to ensure data handling meets privacy and regulatory obligations in Baldwin's service-driven environment.
· Champion transparency across the enterprise: published data owners, documented lineage, and a known answer to “where did this number come from?”
· Own the enterprise business intelligence and reporting capability, including Power BI, and hold it to a standard of accuracy and timeliness the business can plan against.
· Create new data-driven approaches to generating business insight through analysis, information visualization, and proactive attention to unanswered business questions rather than reactive report production.
· Surface patterns in retention, producer performance, carrier profitability, and operating efficiency before someone has to ask for them.
· Support business intelligence and data democratization across the enterprise through governed self-service: certified datasets, discoverable data products, and the enablement colleagues need to answer their own questions safely.
· Serve as the primary data partner to business unit leaders across our retail brokerage platforms and Growth Services functions, translating business needs into a sequenced delivery plan.
· Build the connective tissue- knowledge graph, vector stores, feature stores, and retrieval infrastructure that lets Agentic AI and generative AI use cases consume governed, reliable data.
· Partner with the AI Role Transformation team to translate prioritized AI use cases into concrete data requirements and delivery plans, and be clear about what is and is not ready.
· Support responsible AI guardrails from the data layer up, provenance, access controls, bias-relevant data quality checks, human-in-the-loop review, and auditability of what a model was given.
· Track the data platform's contribution to AI use case delivery, tying the work back to measurable business outcomes.
· Maintain accountability for the enterprise data and analytics technology portfolio, including platforms such as Databricks, Snowflake, and Power BI, along with graph and data management tooling and emerging AI-enabled capabilities.
· Execute modernization plans that advance our cloud-based data platforms and next-generation analytical capabilities, ensuring the ecosystem remains scalable, resilient, and positioned for growth.
· Evaluate emerging data technologies and recommend platform change with evidence, working within the enterprise architecture standards set by the CTO organization.
· Hold vendors accountable to their commitments, independently evaluating technical claims and negotiating from a position of understanding.
· Recruit, coach, and grow a hands-on data engineering, governance, and business intelligence team, leading with a genuine We Before Me mindset.
· Build organizational data fluency and standardize engineering practices to accelerate time-to-value for data and AI initiatives across the firm.
· Model exceptional leadership by balancing delivery with development, recognizing colleague strengths, and fostering a culture of accountability, collaboration, and continuous learning.
· Lead through the lens of the Azimuth and Baldwin's core values - Discerning, Grit, Genuine, Engaging, Purpose, and Dreaming while building a durable data engineering discipline.
· Inspire teams to deliver Baldwin Alchemy: achieving more together than any of us could achieve alone.
· Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field required; advanced degree a plus.
· 10+ years of progressive data engineering and analytics leadership, including direct accountability for a production enterprise data platform — not advisory or program-management-only exposure.
· Hands-on experience building and operating a production lakehouse or cloud data warehouse at enterprise scale; Databricks strongly preferred, Snowflake or equivalent considered.
· Direct experience building out an enterprise ontology and knowledge graph, entity and relationship modeling, ontology definition, entity resolution, graph population from source systems, and downstream consumption by analytics or AI applications. Candidates should be able to describe a graph they built, what it was used for, and what they would do differently.
· Proven track record standing up medallion architectures, data governance programs, and semantic or metrics layers at enterprise scale in an environment where an auditor or regulator eventually asked to see the evidence.
· Experience owning an enterprise business intelligence and reporting function, including a governed self-service model; Power BI experience preferred.
· Experience in a regulated or service-driven industry - insurance, financial services, or healthcare strongly preferred.
· Experience building the data infrastructure - knowledge graphs, vector stores, feature stores, retrieval layers that underpins production AI/ML or generative AI use cases, including graph-based retrieval for LLM applications, preferred.
· Experience integrating data from acquisitions in a high-M&A environment, and familiarity with insurance data structures policy administration systems, agency management systems, carrier feeds, and commission data a plus.
· Deep, hands-on fluency with Databricks, cloud data platforms, and modern data engineering practices - a genuine builder's instinct, not just a strategist's. You have personally been in the platform, and you can still read a query plan.
· Strong semantic and graph data modeling capability: ontology and taxonomy design, labeled property graphs or RDF/OWL, graph query languages such as Cypher or SPARQL, and the judgment to know where a graph earns its keep and where a well-modeled relational layer is sufficient.
· Working knowledge of AI/ML and generative AI data requirements including how ontologies and knowledge graphs improve grounding, explainability, and agent reasoning - sufficient to partner credibly with the AI transformation team and challenge a vendor's claims. Model development and AI strategy sit elsewhere in the organization.
· Practical command of data governance, quality, lineage, and access controls in a regulated environment, with the judgment to know which controls matter and which are theater.
· Exceptional people leadership: engaging, Genuine, and forthright, able to speak straight while building trust, developing talent, and retaining strong engineers in a competitive market.
· Executive-grade communication: able to explain a platform trade-off to a business unit president, translate a technical constraint into business terms, and give the CTO an honest read on what will and will not land.
· Strong operational and financial acumen, including experience managing platform cost, consumption forecasting, and a multi-year technology roadmap.
· A growth mindset with Grit and resilience, continuously turning today's capabilities into tomorrow's advantage.
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The Baldwin Group will not accept unsolicited resumes from any source other than directly from a candidate who applies on our career site. Any unsolicited resumes sent to The Baldwin Group, including unsolicited resumes sent via any source from an Agency, will not be considered and are not subject to any fees for any placement resulting from the receipt of an unsolicited resume.
