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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Director, BI and Advance Analytics - **Company:** Kestra Financial - **Location:** Austin, TX, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Microsoft Azure, Business Intelligence Development, Big Data, Cloud Engineering, Information Engineering, Data Governance, Metadata, Software Deployment, Tableau (Software), Generative AI, Data Analytics, Virtual Agents, Databricks - **Published:** September 2, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=d997ab101f740537 ## About the Role * 12+ years of experience in Business Intelligence, Analytics, Data or Decision Intelligence, including 5+ years leading enterprise analytics organizations. * Deep expertise in Wealth Management, Financial Services, Broker-Dealer, RIA, Custodial, Asset Management or Investment Advisory businesses. * Proven success building BI and Analytics capabilities and influencing senior executives through data-driven recommendations. * Strong knowledge of wealth operations, advisor lifecycle, AUM/AUA, NNA, recruiting, retention, practice management, revenue and profitability analytics. * Exceptional communication, executive presence, people leadership and cross-functional delivery skills., Tableau Next/Tableau and Databricks Lakehouse; Azure; semantic layer, business glossary and KPI standardization; executive and board scorecards; Advanced Analytics, Generative AI, Agentic AI or Decision Intelligence programs; experience within a leading wealth management platform, custodian, broker-dealer, asset manager or large RIA; MBA or relevant advanced degree preferred., Internal applicants must be in good standing and have a minimum of 1 year of service with Kestra. Internal applicants must also have a minimum of 1 year service in current role unless approved by EVP. ## Description Strategic Impact: Shape decisions that drive advisor growth, client acquisition, operational efficiency and enterprise transformation. Innovation at Scale: Lead initiatives using Tableau Next, Databricks Lakehouse, governed semantic models, Generative AI and Decision Intelligence. Executive Visibility: Partner directly with executive, line-of-business and technology leaders to deliver measurable outcomes. Leadership Opportunity: Lead and develop BI, analytics engineering, analytics product and advanced analytics capabilities. Growth & Transformation: Modernize wealth management analytics and help establish an AI-enabled, data-driven organization. Key Leadership Responsibilities: Analytics, AI & Business Value Strategy * Define and execute an enterprise Analytics and AI roadmap aligned to advisor growth, client experience, operational efficiency and business performance. * Prioritize predictive, prescriptive, Generative AI and Agentic AI use cases; establish value measures that quantify adoption and outcomes. * Partner with executives to embed decision intelligence and governed self-service analytics into business workflows. Enterprise BI & Semantic Models * Own Tableau Next strategy and delivery of executive dashboards, board reporting, operational scorecards and self-service analytics. * Lead enterprise semantic models and reusable business metrics on the Databricks Lakehouse platform. * Deliver business-ready data products supporting advisor productivity, recruiting, retention, AUM/AUA, NNA, revenue, client engagement, compliance, practice management and operations. * Establish KPI governance, metric standardization and a trusted source of truth across business functions. Wealth Management Analytics Leadership * Serve as a trusted advisor on advisor performance, recruiting effectiveness, retention, practice growth, asset flows, profitability, client acquisition, compliance and supervision. * Partner across Finance, Operations, Product, Compliance and Field Leadership to turn analytical findings into business action. Data Products & Cross-Functional Partnership * The Director is accountable for analytics product strategy, business outcomes, user adoption and value realization. * Delivery will use an integrated operating model with shared roadmaps, backlogs, decision rights, quality gates and release criteria across the following teams: Collaboration with Data Governance: * Co-own KPI definitions, business glossary, data ownership and stewardship, quality rules, metadata, lineage, access controls and responsible AI requirements. * Ensure each product has approved definitions, accountable owners and transparent controls. Collaboration with Data Engineering, Data Products & Architecture: * Translate product requirements into curated datasets, scalable pipelines and semantic-ready structures on Databricks. * Jointly manage dependencies, nonfunctional requirements, performance, reliability, release readiness and production support. Data Science, Security & Compliance: * Operationalize advanced analytics and AI while embedding privacy, cybersecurity, model risk, supervision and regulatory requirements throughout design, deployment and monitoring. Team Leadership, Inherited Capability & Future Growth * Assume leadership of Kestra's existing BI and analytics delivery capability and assess current skills, capacity, role clarity and ways of working. * Create a cohesive operating model spanning BI development, analytics engineering, advanced analytics and analytics product management. * Mentor leaders and individual contributors; establish career paths, training, succession readiness and a culture of innovation, accountability and collaboration. * Develop a future capacity and talent plan aligned to demand and value realization. Potential expansion may include BI developers, analytics engineers, analytics product managers, data scientists and AI/decision-intelligence specialists, subject to approved workforce plans. * Establish delivery governance that produces scalable, trusted and well-adopted analytics products. Technical Leadership * Provide strategic oversight of Tableau Next, enterprise reporting architecture, semantic modeling and metric governance. * Leverage Databricks Lakehouse and Azure to enable scalable analytics and AI workloads. * Establish standards for data quality, metadata, governance, lineage, performance and reliability in partnership with Engineering and Architecture. * Drive responsible modernization through cloud-native and AI-enabled technologies., Success Metrics & Outcomes Business Impact: Measurable improvement in advisor productivity and contribution to AUM, AUA, NNA, revenue or operating outcomes. Analytics Transformation: Adoption of Tableau Next, standardized KPIs, governed semantic models, self-service analytics and reusable enterprise data products. AI & Innovation: Production deployment of high-value AI use cases with responsible controls, user adoption and quantified benefits. Leadership Excellence: A high-performing, scalable team with strong engagement, delivery discipline and succession readiness.