Director of Enterprise Data ManagementOpportunity ID
10276
Department
Practice Management
Location(s)
Dallas
State
Texas
Function
Information Technology
Job Description
We currently have an exciting career opportunity for a
Director of Enterprise Data Management to join our
Global Digital & Cyber team.
CohnReznick is a hybrid firm and most of our professionals are located within a commutable distance to one of our offices. This position is considered remote which means it does not require job duties be performed within proximity of a CohnReznick office location. However, as a remote employee, you may be required to be present at a CohnReznick office with scheduled notice for client work, team meetings, or trainings.
YOUR TEAM. The Director Enterprise Data is a senior technology leader responsible for establishing and leading the firm's enterprise data capability. This position defines the vision, strategy, operating model, architecture, governance, talent plan, and delivery roadmap required to transform the firm's data into a trusted, reusable, and differentiated enterprise asset.
The position is accountable for creating an authoritative and secure data foundation that improves decision-making, enables automation, strengthens client service, and gives the firm a sustainable AI advantage. The leader will establish the platforms, data products, standards, controls, and organizational capabilities required to make high-value firm and client data discoverable, understandable, accessible, interoperable, and fit for human and machine use.
This is not a traditional business intelligence, data warehousing, or reporting leadership role. The successful candidate must have demonstrated experience building an enterprise data capability that converted fragmented information into governed, reusable data products and measurable business outcomes. The role requires an equal focus on business value, data product management, modern data engineering, governance, quality, responsible use, and enterprise adoption.
The leader will work closely with AI Software Development, Product Management, Enterprise Architecture, Information Security, Risk, Legal, and leaders across Tax, Assurance, and Advisory. Together, these functions will create a data foundation that accelerates AI-enabled products, intelligent automation, analytics, knowledge discovery, and new technology-enabled services.
YOUR ROLE. Responsibilities include but not limited to:Enterprise data strategy and value creation- Define and execute a multi-year enterprise data strategy aligned with firm business, AI, automation, technology, risk, and growth objectives.
- Establish a compelling vision for data as a proprietary firm asset and a source of sustained competitive advantage.
- Translate the strategy into a sequenced roadmap of enterprise data products, platform capabilities, governance improvements, and business outcomes.
- Identify the firm's highest-value data domains and prioritize investments based on client impact, professional productivity, quality, growth, automation potential, AI enablement, and risk reduction.
- Establish investment cases, a funding model, capacity plan, governance forums, and performance measures.
- Advise executive leadership on data opportunities, dependencies, economics, risks, ownership decisions, and portfolio trade-offs.
Build the firm's strategic data asset- Create a connected data foundation spanning structured data, documents, knowledge content, communications, workflow information, and other appropriate firm information assets.
- Establish authoritative data products for critical domains with defined ownership, intended uses, quality expectations, service levels, controls, and lifecycle accountability.
- Develop common definitions, reference and master data, taxonomies, ontologies, identifiers, and semantic models.
- Reduce unnecessary duplication and fragmentation while preserving appropriate domain ownership and regulatory boundaries.
- Enable controlled reuse of firm and client data across products, workflows, analytics, automation, and AI solutions.
- Increase the asset's value over time through reuse, feedback, improved quality, expanded context, and connection to business processes.
AI- and automation-ready data- Establish the data capabilities needed for generative AI, agentic workflows, machine learning, advanced analytics, intelligent search, and process automation.
- Partner with AI Software Development and Product Management to convert priority use-case requirements into reusable enterprise capabilities.
- Define standards for retrieval-augmented generation, semantic search, approved model training, evaluation, grounding, and automated decision support.
- Deliver secure methods for AI systems and digital workers to discover, retrieve, interpret, and act upon authorized information.
- Preserve confidentiality, privilege, independence, client restrictions, engagement boundaries, retention requirements, and professional obligations.
- Use consumption feedback to improve data quality, relevance, context, and usability.
- Ensure individual AI investments contribute to a reusable data foundation rather than isolated pipelines or one-off datasets.
Data product management and domain ownership- Implement a data-product operating model with named business owners, stewards, technical owners, roadmaps, consumers, service expectations, and value measures.
- Partner with Tax, Assurance, Advisory, Finance, People, Risk, Marketing, and other functions to prioritize domain data products.
- Establish discovery, intake, prioritization, funding, design, release, adoption, support, and continuous-improvement practices.
- Balance enterprise consistency with specialized service-line, industry, functional, engagement, and jurisdictional needs.
- Hold data product teams accountable for usability, trust, adoption, reliability, value, and responsible use.
- Enable professionals and product teams to discover data products and understand definitions, provenance, restrictions, quality, and approved uses.
Data platform architecture and engineering- Define the enterprise data architecture across ingestion, integration, storage, transformation, access, metadata, lineage, quality, semantic, serving, and analytical capabilities.
- Establish reference architectures, engineering standards, reusable patterns, APIs, data contracts, and lifecycle practices.
- Lead modern cloud data platforms supporting operational, analytical, AI, knowledge, and automation workloads.
- Promote automation, observability, testing, version control, DataOps, infrastructure as code, secure deployment, and production reliability.
- Establish standards for batch, streaming, API-based, document-based, and event-driven data movement.
- Drive consolidation and reuse that improve economics, interoperability, quality, security, supportability, or speed.
- Manage performance, scalability, resilience, technical debt, lifecycle risk, vendor dependencies, and cost.
Data governance, quality, and trust- Establish practical governance that enables responsible use and rapid delivery rather than operating only as a compliance activity.
- Define decision rights for ownership, stewardship, access, quality, classification, retention, sharing, and approved use.
- Implement metadata, business glossaries, lineage, cataloging, quality monitoring, issue management, and policy enforcement.
- Establish risk-based quality standards based on the materiality and intended use of each data product.
- Integrate data governance with AI governance, privacy, security, legal, records, independence, regulatory, contractual, and client obligations.
- Create transparent processes for ownership disputes, access issues, quality defects, policy exceptions, and cross-domain dependencies.
- Develop data literacy and stewardship practices across the firm.
- Report clearly on data health, risks, adoption, investment, and realized value.
Organization and talent leadership- Recruit, organize, develop, and retain data product leaders, architects, engineers, platform specialists, governance professionals, metadata specialists, quality leads, and domain stewards.
- Define organization design, job architecture, role expectations, career paths, leadership spans, technical communities, and development plans.
- Establish accountability across centralized platform teams, federated domain teams, control functions, and strategic partners.
- Create a culture centered on value, product thinking, engineering discipline, trust, reuse, service, accountability, and continuous improvement.
- Build a sustainable talent pipeline and future data leaders.
- Lead geographically distributed employees, contractors, managed services, and strategic partners.
Enterprise influence and change leadership- Build trusted partnerships with executives, service-line leaders, client-service professionals, technology teams, and control functions.
- Explain the connection between data investment and AI, automation, client service, quality, risk, productivity, and growth.
- Establish shared accountability for data outcomes across business and technology.
- Lead adoption of common definitions, authoritative data products, stewardship practices, and new ways of consuming information.
- Drive adoption through communications, training, community building, embedded support, and transparent reporting.
- Represent Enterprise Data in senior governance, investment, architecture, risk, and strategic-planning forums.
The duties listed are intended to describe the general nature and level of work performed. They are not an exhaustive list of every responsibility, duty, or skill required. Responsibilities may change based on business needs.
YOUR EXPERIENCE. The successful candidate will have:Education and experience- Bachelor's degree in Computer Science, Data Science, Engineering, Information Systems, Analytics, Business, or a related field, or equivalent relevant experience.
- Fifteen or more years of progressive experience in data, analytics, technology, architecture, engineering, or related leadership roles.
- Seven or more years leading enterprise data, data platform, analytics, governance, or data engineering organizations, including leaders of teams.
- Demonstrated success building or materially transforming an enterprise data capability in a complex organization.
- Proven delivery of governed data products and platforms into production with measurable adoption and value.
- Experience developing enterprise data strategies across multiple business domains.
- Experience in complex, regulated, confidential, or high-trust environments.
- Experience leading distributed teams and blended workforces.
- Ability to influence senior business and technology leaders across organizational boundaries.
Data strategy and product expertise- Enterprise data strategy, operating models, portfolio management, funding, and value realization.
- Data product management, domain design, business ownership, stewardship, service levels, adoption, and lifecycle management.
- Master and reference data, metadata, cataloging, lineage, taxonomy, ontology, semantic layers, and business glossaries.
- Data quality management, observability, issue remediation, control design, and data-health measurement.
- Data literacy, organizational change, communications, and adoption.
Technical and architectural expertise- Modern cloud data architectures, lakes, lakehouses, warehouses, operational stores, fabrics, or comparable patterns.
- Data ingestion, integration, APIs, data contracts, event streaming, transformation, orchestration, and serving.
- Structured and unstructured data, document intelligence, knowledge systems, enterprise search, and content enrichment.
- Data engineering, analytics engineering, DataOps, automated testing, deployment automation, observability, reliability, and cost management.
- Data security, identity, access controls, encryption, classification, privacy, retention, information barriers, and secure sharing.
- AI data requirements including retrieval-augmented generation, vector and semantic retrieval, agentic solutions, evaluation, and intelligent automation.
- Experience making build, buy, configure, and partner decisions.
Governance, risk, and professional-services expertise- Enterprise data governance, pol
