Managing Director Jobs in San Francisco, CA

Overview

Baker Tilly is a leading advisory, tax and assurance firm, providing clients with a genuine coast-to-coast and global advantage in major regions of the U.S. and in many of the world’s leading financial centers – New York, London, San Francisco, Los Angeles, Chicago and Boston. Baker Tilly Advisory Group, LP and Baker Tilly US, LLP (Baker Tilly) provide professional services through an alternative practice structure in accordance with the AICPA Code of Professional Conduct and applicable laws, regulations and professional standards. Baker Tilly US, LLP is a licensed independent CPA firm that provides attest services to its clients. Baker Tilly Advisory Group, LP and its subsidiary entities provide tax and business advisory services to their clients. Baker Tilly Advisory Group, LP and its subsidiary entities are not licensed CPA firms.

Baker Tilly Advisory Group, LP and Baker Tilly US, LLP, trading as Baker Tilly, are independent members of Baker Tilly International, a worldwide network of independent accounting and business advisory firms in 141 territories, with 43,000 professionals and a combined worldwide revenue of $5.2 billion. Visit bakertilly.com or join the conversation on LinkedIn, Facebook and Instagram.

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Job Description

Baker Tilly is seeking a Managing Director – Enterprise Architecture, Data & AI Governance to lead the firm’s enterprise architecture function and establish the architectural and governance foundation required to scale technology, data, and artificial intelligence responsibly across the enterprise.

Reporting directly to the Chief Digital & Information Officer (CDIO), this leader will serve as the firm’s senior authority for enterprise architecture and will bring together Enterprise Architecture, Data Governance, and AI Governance into an integrated enterprise capability. The role will establish the standards, guardrails, decision rights, controls, and governance mechanisms that allow Baker Tilly to innovate rapidly while maintaining enterprise integrity, interoperability, security, data quality, responsible AI practices, operational sustainability, and the control discipline expected of a future publicly traded company.

As Baker Tilly prepares for the possibility of becoming a publicly traded company, this leader will help mature technology, data, and AI governance to public-company standards. The role will partner closely with Innovation, Finance, Risk, Legal, Privacy, Security, Internal Audit, and other control functions to establish durable evidence, accountability, traceability, and control practices that can withstand executive, board, auditor, regulator, and investor scrutiny.

This role is central to Baker Tilly’s evolving technology operating model. Enterprise Architecture will operate as an enabling enterprise function—not simply an architecture review body—providing reusable standards and patterns, engaging early with capability and product teams, governing critical architectural decisions, and creating clear paths that allow teams to move quickly within established enterprise guardrails.

A critical element of the mandate is maintaining a clear architectural seam between core Enterprise Technology and emerging AI Innovation in partnership with Baker Tilly’s AI Product Group (APG).

Strategic Mandate

• Establish an integrated enterprise architecture function that defines Baker Tilly’s target technology architecture and provides clear standards, patterns, roadmaps, and guardrails across the technology estate.
• Own enterprise Data & AI Governance, creating a coherent governance model covering enterprise data standards, responsible AI, risk-tiered AI and agent approval, architecture compliance, technology lifecycle management and production readiness.
• Define and steward the enterprise data architecture, including the enterprise data foundation, data contracts, integration patterns, systems of record, data interoperability, and BTID as the firm’s client-data join key and source-of-truth mechanism.
• Enable rather than impede technology delivery by embedding architecture and governance early in capability, platform, transformation, and AI product lifecycles and providing pre-approved patterns and pathways wherever practical.
• Establish the architectural partnership between Enterprise Technology and the AI Product Group, ensuring that AI products can innovate rapidly while securely and sustainably operating at enterprise scale.
• Reduce technology complexity and fragmentation by driving standardization, reuse, platform leverage, application rationalization, and alignment to target-state architecture.
• Provide senior architectural leadership to the CDIO and enterprise leadership, translating technology, data, and AI architecture into clear business implications, investment choices, risks, and strategic opportunities.
• Build public-company-ready technology, data, and AI governance capabilities, including clear control ownership in partnership with the Risk Organization, documented decision rights, evidence-based compliance, auditability, and disciplined oversight of technology and data risks.

Core Responsibilities

Enterprise Architecture Strategy & Leadership

• Define and maintain Baker Tilly’s enterprise technology architecture, principles, standards, and target-state roadmaps.
• Establish architecture across major domains including applications, platforms, cloud, integration, APIs, enterprise data and identity.
• Create a coherent view of the firm’s current-state and target-state technology landscape and connect architectural roadmaps to business strategy, capability roadmaps, platform investments, and major transformation programs.
• Identify opportunities to simplify the technology estate and reduce redundant applications, platforms, integrations, and architectural patterns.
• Advise the CDIO and other senior stakeholders on significant architectural choices, technology investments, technical debt, and modernization priorities.
• Maintain architecture decision records and mechanisms that make significant enterprise technology decisions transparent and durable.

Enterprise Data Architecture & Governance

• Establish and steward the enterprise data architecture in partnership with Business Technology, Operational IT, APG, Security, and business stakeholders.
• Define architectural standards for the enterprise data foundation, systems of record, data domains, data contracts, integration, APIs, metadata, lineage, quality, and interoperability.
• Establish enterprise principles governing authoritative data sources, ownership, stewardship, quality, classification, access, retention, and lifecycle management.
• Define and govern standards supporting BTID as the firm’s client-data source-of-truth and cross-platform join mechanism.
• Ensure APG and other product teams can consume enterprise data through governed, scalable, published data contracts.

AI Governance & Responsible AI

• Lead the enterprise AI Governance framework in partnership with Security, Risk, Legal, Compliance, APG, and other stakeholders.
• Manage a risk-tiered governance model for AI systems and agents, allowing lower-risk experimentation to move quickly through pre-approved paths while applying appropriate scrutiny to higher-risk use cases.
• Establish a single enterprise launch-gate approach applicable to internally developed AI, agents, vendor AI capabilities, and other AI-enabled systems.
• Define governance standards covering AI risk classification, data use, privacy, security, model evaluation, human oversight, explainability, monitoring, and production readiness.
• Partner with APG on evaluation and production criteria: APG develops product-specific evaluation approaches while Enterprise Architecture ratifies enterprise standards and production-gate requirements.
• Maintain clear accountability between governance and engineering: governance defines the enterprise bar; product/capability and engineering teams remain accountable for designing and building solutions that meet it.

Architecture Governance & Production Readiness

• Establish practical architecture governance integrated into Baker Tilly’s capability operating model and clarify architectural decision rights, including advisory versus approval authority.
• Create reusable reference architectures, patterns, standards, and pre-approved approaches that allow teams to operate independently within enterprise guardrails.
• Participate early in major product, platform, transformation, and capability roadmaps to identify architectural implications and dependencies before delivery commitments are made.
• Establish architecture and non-functional requirements for scalability, deployment, integration, resilience, security, observability, supportability, and operational readiness.
• Partner with Operational IT and APG to define and maintain the production handoff standard for new products and platforms.
• Co-manage appropriate production gates and evaluate definition-of-done from an enterprise architecture and governance perspective.

Partnership with the AI Product Group

• Serve as the principal enterprise architecture partner to the Chief AI and Technology Officer and AI Product Group.
• Maintain the explicit architectural boundary: APG owns AI product architecture, agentic architecture, the AI intelligence substrate, ontology, knowledge graph, judgment layer, and product data architecture; Enterprise Architecture owns enterprise architecture, enterprise integration, systems-of-record architecture, enterprise data foundation architecture, enterprise standards, Data Governance, and AI Governance.
• Partner with APG on agentic architecture through standards and gates rather than assuming design authority inside APG products.
• Ensure AI products can transition cleanly from experimentation and product engineering into enterprise production and operations.
• Help create reusable enterprise patterns so each new AI product becomes progressively easier, faster, and less expensive to scale.

Capability Operating Model Enablement

• Position Enterprise Architecture as a horizontal enabling capability supporting product, platform, and service teams across TDIO.
• Provide architects to capability teams as appropriate while maintaining enterprise consistency and an architecture community of practice.
• Participate in Quarterly Technology Reviews and other cross-team planning mechanisms to identify architectural dependencies and constraints early.
• Partner with Capability Owners and Technical Leads to translate enterprise architecture into actionable team-level guidance.
• Establish interaction models that make it clear when teams can proceed independently, when consultation is required, and when enterprise approval is necessary.
• Measure Enterprise Architecture success based on enablement, reuse, simplification, risk reduction, and business outcomes—not architecture-review volume.

Technology Simplification & Modernization

• Establish an enterprise approach to technology and application rationalization.
• Identify duplication, fragmentation, obsolete platforms, unnecessary customization, and opportunities for consolidation.
• Drive greater reuse of enterprise platforms, APIs, integration services, data capabilities, and architectural patterns.
• Partner with Strategy & Planning to connect architectural recommendations to portfolio and investment decisions.
• Develop modernization roadmaps that balance business value, risk reduction, technical debt, cost, and organizational capacity.

Leadership & Organizational Development

• Build and lead a high-performing Enterprise Architecture and Governance organization.
• Develop enterprise, domain, solution, data, and governance talent appropriate to the future operating model.
• Establish an architecture community of practice across TDIO and APG and create clear roles and career paths for architects.
• Build a culture in which architects are viewed as strategic partners and enablers rather than reviewers operating outside delivery teams.

Public Company Readiness, Controls & Assurance

• Lead the maturation of enterprise architecture, data, and AI governance practices to support Baker Tilly’s longer-term IPO and public-company readiness objectives.
• Bring practical experience operating in, preparing for, or advising a publicly traded company, IPO candidate, or similarly rigorous regulated environment with mature technology controls and governance expectations.
• Partner with Finance, Internal Audit, Risk, Legal, Privacy, Security, and Compliance to define technology and data controls that support financial reporting integrity, regulatory obligations, audit readiness, and executive/board oversight.
• Ensure architecture and governance processes produce durable evidence of decisions, approvals, exceptions, risk acceptance, control execution, and remediation.
• Establish clear ownership and traceability for systems of record, critical data, integrations, privileged access dependencies, change controls, third-party technology, and AI-enabled processes that could affect financial, operational, regulatory, or client outcomes.
• Support technology-control readiness associated with public-company expectations, including IT general controls, automated/application controls, change management, access governance, data integrity, third-party risk, resilience, and control monitoring.
• Provide credible technology, architecture, data, and AI governance leadership for board, audit committee, investor diligence, external audit, and other assurance activities as Baker Tilly’s governance model matures.

Innovation, Risk, Legal & Privacy Partnership

• Create an intentional partnership with BT Labs, APG Design & Innovation, Applied AI, and product teams so architecture, privacy, legal, security, and responsible-AI considerations are incorporated from concept and experimentation through production scale.
• Design governance pathways that protect experimentation velocity: low-risk concepts should have pre-approved patterns and lightweight controls, while higher-risk uses receive deeper review based on data sensitivity, client impact, autonomy, materiality, and regulatory exposure.
• Partner with Legal, Privacy, Risk, and Security on data privacy and data protection requirements, including appropriate use, collection, classification, access, retention, sharing, cross-border considerations, vendor use, and protection of client, employee, and firm-confidential information.
• Ensure innovation involving AI, data, models, agents, and third-party platforms has clear ownership for intellectual property, confidentiality, contractual obligations, data rights, acceptable use, human oversight, and production accountability.
• Translate regulatory, legal, privacy, and risk requirements into practical architecture patterns and engineering guardrails rather than relying solely on downstream review.
• Maintain a clear escalation and exception process for novel technologies where existing standards do not yet provide sufficient guidance, enabling informed risk-taking with documented accountability.

Key Organizational Interfaces

• Chief Digital & Information Officer (CDIO): direct executive sponsor; alignment on enterprise technology strategy, architecture, investment, governance, and modernization.
• Chief AI and Technology Officer (CAITO): peer technology organization leader with interactions focused on AI product architecture, enterprise standards, data, governance, and production scale.
• AI Product Group: architectural standards, enterprise data contracts, AI governance, production gates, and enterprise integration.
• Operational IT: cloud, infrastructure, workplace, support, deployment, reliability, observability, and operational architecture.
• Business Technology: enterprise applications, systems of record, enterprise data foundation, and integration with core business processes.
• Service-Line Technology: architectural guidance, standards, modernization, reuse, and integration across Tax, Assurance, Consulting, and Managed Services.
• Security / CISO: security architecture, risk management, secure-by-design standards, and AI/data risk.
• Strategy & Planning: portfolio planning, investment prioritization, QTRs, value realization, vendor strategy, and technology simplification.
• Risk, Legal & Compliance: responsible AI, privacy, regulatory obligations, and enterprise governance requirements.

Required Experience

• 15+ years of progressive leadership experience across enterprise architecture, technology strategy, data architecture, engineering, or related disciplines.
• Bachelors Degree required.
• Demonstrated leadership experience in a publicly traded company, IPO-readiness environment, or organization operating with comparable public-company control, audit, governance, and disclosure rigor; direct participation in IPO preparation or public-company technology/control maturation strongly preferred.
• Working knowledge of public-company technology control expectations, including IT general controls, access and change controls, application/automated controls, evidence retention, auditability, control testing, remediation, and the technology implications of financial reporting controls.
• Experience partnering credibly with CFO/Finance, Internal Audit, external audit, Risk, Legal, Privacy, Security, and Compliance functions on enterprise technology, data, and control matters.
• Experience presenting technology risk, architecture, data governance, cyber/privacy dependencies, and control posture to executive leadership and, ideally, boards or audit/risk committees.
• Demonstrated experience integrating data privacy and data protection requirements into architecture, data governance, vendor governance, cloud, analytics, and AI delivery practices.
• Experience establishing governance for emerging technology and innovation that balances speed with legal, regulatory, privacy, security, intellectual-property, and reputational considerations.
• Significant experience leading Enterprise Architecture in a complex, distributed, matrixed organization.
• Demonstrated experience defining enterprise technology strategy and translating it into executable target-state architectures and modernization roadmaps.
• Deep experience with enterprise data architecture, including modern cloud data platforms, integration patterns, APIs, data domains, data contracts, metadata, lineage, and governance.
• Demonstrated experience establishing architecture governance that balances enterprise control with delivery speed and team autonomy.
• Strong understanding of modern AI architectures, generative AI, agentic systems, AI platforms, and the governance implications of deploying AI in enterprise environments.
• Experience developing or operating Data Governance and/or Responsible AI governance programs.
• Demonstrated ability to establish enterprise standards while partnering effectively with autonomous product and engineering organizations.
• Experience influencing major technology investment, modernization, simplification, and platform decisions.
• Proven ability to operate effectively with senior executives, business leaders, architects, engineers, security leaders, risk professionals, and product teams.
• Experience leading through significant organizational and operating-model transformation.
• Experience in professional services, financial services, SaaS, or another highly regulated or data-intensive environment preferred.

Technical & Domain Competencies

• Enterprise and domain architecture
• Modern cloud and distributed architectures
• Enterprise data architecture and modern data platforms
• Data governance, metadata, lineage, quality, and stewardship
• Integration architecture, APIs, and event-driven architecture
• AI and generative AI architecture
• Agentic AI patterns and governance
• Responsible AI frameworks and model governance
• Architecture governance and decision-rights frameworks
• Application and technology portfolio rationalization
• Platform engineering and DevOps concepts
• Security and privacy architecture
• Technology modernization and technical-debt management
• Architecture tooling, repositories, standards, and roadmapping
• Public-company / IPO technology governance and control readiness
• IT general controls, automated/application controls, audit evidence, and control remediation
• Data privacy, data protection, information lifecycle, and cross-border data considerations
• Third-party technology, data, AI, and vendor risk governance

Leadership Attributes

• Enterprise-minded: optimizes for the firm rather than individual platforms, functions, or organizational boundaries.
• Enabling rather than bureaucratic: understands that good governance creates speed by making the safe path clear and reusable.
• Technically credible: can engage deeply with architects and engineers while communicating implications clearly to executive leadership.
• Pragmatic: distinguishes between decisions requiring enterprise control and decisions best left to empowered teams.
• Strategic: connects architecture decisions to business strategy, investment choices, risk, operating leverage, and long-term enterprise value.
• Collaborative: succeeds through influence across the CDIO, CAITO, Security, Risk, business, and technology organizations.
• Decisive: willing to establish standards, resolve architectural ambiguity, and make difficult simplification decisions.
• Future-oriented: understands how AI, agents, modern data architecture, and rapidly evolving technology will reshape enterprise architecture.
• Talent builder: develops architects who combine technical depth, business acumen, communication skills, and an enterprise mindset.
• Clear communicator: makes complex architecture, data, and AI topics understandable and actionable for executive stakeholders.

Success Metrics

• Adoption and reuse of enterprise architecture standards and patterns.
• Reduction in technology duplication, fragmentation, and unnecessary complexity.
• Progress against target-state architecture and modernization roadmaps.
• Improved architectural alignment of major technology investments.
• Increased use of shared platforms, integration patterns, APIs, and enterprise data capabilities.
• Improved quality, accessibility, interoperability, and governance of enterprise data.
• Percentage of material AI initiatives operating through the defined risk-tiered governance framework.
• Speed and predictability of AI and technology governance decisions.
• Reduction in late-stage architecture, data, security, and operational-readiness issues.
• Successful implementation and adoption of the production handoff standard.
• Effectiveness of the architecture partnership between TDIO and APG.
• Stakeholder confidence in Enterprise Architecture as an enabling strategic partner.
• Measurable contribution to technology simplification, operational efficiency, risk reduction, and enterprise value creation.
• Progress toward public-company-ready technology, data, and AI control maturity, with documented ownership, evidence, testing, and remediation mechanisms.
• Reduction in material control gaps, repeat audit findings, privacy/data-protection exceptions, and unmanaged technology or AI risks.
• Demonstrated ability to accelerate innovation through risk-tiered, pre-approved governance pathways while maintaining appropriate Legal, Risk, Privacy, and Security oversight.

Compensation

The national pay rate range for this