Director of Data Services

Director Of Data Services

Based in Los Angeles, New York or Washington, D.C., the Director Of Data Services will be responsible for managing the way Gibson Dunn captures, governs, integrates, and activates its data across every financial system, matter, client relationship, and AI model that the firm deploys. This role will establish the data foundation upon which the firm's AI program, business intelligence capability, and operational decision-making rest. This executive-level role will build the firm's first enterprise-grade data architecture, create a lasting data governance culture, and deliver analytics products that give firm leadership and practice group heads a level of strategic intelligence the firm has never had before.

This role reports to the Chief Legal Operations Officer.

Responsibilities Include:

Enterprise Data Strategy

• Owning and continuously evolving the firm's enterprise data strategy and multi-year roadmap approved by firm leadership, integrating with the AI strategy, and aligning to the firm's financial, operational, and competitive priorities.
• Advising the CLOO on data's strategic implications including: the value of data assets the firm already holds, the investments required to realize that value, and the risks of continued data fragmentation.
• Establishing Gibson Dunn's data vision by articulating what the firm's data estate will look like at full maturity, what decisions it will enable, and how it will position the firm competitively.
• Building and chairing the firm's Data Governance Council.
• Leading annual data strategy reviews with firm leadership presenting progress, ROI, and updated priorities against the multi-year roadmap.

Data Architecture & Engineering

• Designing and overseeing the build of the firm's enterprise data architecture, creating a unified, governed data layer integrating Aderant (financial/billing), iManage (document management), Intapp (conflicts, NBI, risk), CRM, HR systems, and operational platforms.
• Leading the selection and implementation of modern data infrastructure tools and platforms including cloud data warehouse (Snowflake, Databricks, Azure Synapse, or equivalent), ETL/ELT orchestration, data catalog, and lineage tooling.
• Establishing and enforcing enterprise data standards, taxonomies, and metadata policies, resolving the multi-definition problem affecting key firm metrics such as realization rate, utilization, and client revenue.
• Owning the firm's master data management (MDM) program, establishing authoritative systems of record for clients, matters, attorneys, rates, and timekeeper hierarchy.
• Overseeing the data engineering team and setting technical standards for pipeline development, data quality testing, schema design, and system integration architecture.

Analytics & Business Intelligence

• Owning a Practice Intelligence platform providing firm leadership, practice group co-chairs, and administrative leaders with self-serve access to matter pipelines, utilization, realization, client revenue, lateral productivity, and workforce analytics.
• Partnering with Finance to deliver analytics beyond standard reports, providing partner-level, matter-level, and client-level economic insight that informs compensation, pricing, and staffing decisions.
• Partnering with Business Development to build client intelligence products including relationship analytics, cross-sell opportunity modeling, client revenue trend analysis, and competitive positioning data.
• Partnering with Human Resources to provide workforce analytics including staffing models, associate advancement tracking, DEI reporting, and lateral hiring ROI analysis.
• Establishing a BI Center of Excellence, selecting, governing, and supporting firm BI and visualization tooling (Power BI, Tableau, or equivalent); building a community of data users; and expanding self-serve analytics capability across the firm.

Data Governance & Compliance

• Developing and owning the firm's comprehensive Data Governance Policy, covering data ownership, access controls, classification, retention, lineage, quality standards, and AI training eligibility, reviewed and approved by firm leadership, IT Security, and General Counsel.
• Managing the firm-wide Data Stewardship Program.
• Working with OGC, ensure all data practices comply with GDPR, CCPA, applicable bar confidentiality rules, client contractual data obligations, and the firm's information security policies; serving as the firm's data compliance authority.
• Representing the firm's data interests in enterprise technology decisions, ensuring architecture, security, and governance requirements are built into every major system procurement.

AI Data Enablement

• Serving as an essential executive business partner on AI infrastructure: owning the data layer that powers all firm AI programs, including retrieval-augmented generation pipelines, vector databases, embedding workflows, and the knowledge retrieval infrastructure attorneys use to surface precedents and work product.
• Defining and enforcing data quality, classification, and access-control standards governing which firm data is eligible for AI retrieval, training, or fine-tuning, ensuring all AI use of firm data meets confidentiality, privilege, and security requirements.
• Building and maintaining AI-ready data infrastructure at the pace the AI Team program requires, coordinating with IT to remove data blockers for every AI deployment.
• Owning the firm's data audit logging infrastructure (Harmonic) for AI tools, ensuring complete, auditable records of what data AI tools accessed, when, and by whom.

Executive Leadership

• Building, leading, and developing a high-performing organization of professionals across data governance, analytics, records, and governance, with deep commitment to talent development and succession planning.
• Serving as an executive sponsor for data literacy and data culture initiatives across the firm, demonstrating personally that data-driven decision making is how Gibson Dunn's leadership operates.
• Maintaining a strong external network with data leaders in professional services, technology, and financial services to continuously benchmark the firm's data maturity and import relevant innovations.

Qualifications:

• 15+ years of progressive experience in data engineering, data architecture, analytics, or enterprise data strategy; at least 5 years in a Director, VP, or executive director-level role.
• Proven track record building enterprise data infrastructure from the ground up in a complex, multi-system environment, including data warehouse architecture, ETL/ELT pipeline development, and enterprise data governance.
• Deep expertise in modern data stack technologies: cloud data warehouses (Snowflake, Azure or equivalent), orchestration tools, data catalog and lineage platforms, and BI visualization tooling.
• Proven experience developing and implementing data governance frameworks that achieve lasting organizational adoption, including data ownership models, classification schemas, and stewardship programs.
• Exceptional executive presence: ability to translate data complexity into strategic insight for partners, managing partners, and peers.
• Strong track record building and leading multidisciplinary data organizations spanning engineering, analytics, and governance.
• Professional services, legal, financial services, or other highly regulated, data-sensitive industry experience.

Experience:

• Prior experience at a law firm, in-house legal department, or legal technology company.
• Familiarity with legal systems: Aderant (financial/billing), iManage (DMS), Intapp (conflicts/risk/NBI), or comparable platforms.
• Experience building AI data infrastructure: RAG pipelines, vector databases, embedding workflows, and LLM evaluation data architectures.
• Understanding of attorney-client privilege, confidentiality rules, and professional responsibility as applied to data management and AI.
• Experience with master data management (MDM) in a multi-entity, multi-jurisdictional environment.
• Advanced degree in Data Science, Computer Science, Information Management, Statistics, or related field; or JD/MBA with strong quantitative background.
• Professional certifications in data governance, architecture, or cloud platforms (CDMP, TOGAF, Snowflake SnowPro, AWS/Azure data certifications).