Clifford Capital Hiring! Full Time Director, Data Management Team Lead in - Ricebowl

Position Overview

The Data Management Team Lead serves as Secretariat to the Data Working Management Committee (“DWMC”) and leads the practical delivery of enterprise data management capabilities. The role combines governance coordination, stakeholder management, data lineage, data engineering, analytics enablement, data quality monitoring, and the responsible application of generative artificial intelligence (“GenAI”).

The role holder will translate DWMC priorities into executable workplans and trusted data products, enabling authorised internal users to discover, understand, analyse, and query governed data efficiently, including through natural-language interfaces supported by code.

Key Responsibilities

DWMC Secretariat and Governance

•Manage the DWMC agenda, meetings, papers, minutes, stakeholder inputs, decisions, actions, deadlines, and escalations through to closure.

•Maintain integrated governance registers and produce concise, risk-based reporting for the DWMC and management/board meetings on key trends, breaches, overdue actions, constraints, and decisions required.

•Drive cross-functional resolution and adoption of data governance requirements through effective communication, training, and timely escalation of material issues.

Data Management and AI Enablement

•Data Lineage and GenAI: Lead end-to-end lineage mapping and use GenAI-assisted tools for validated secure metadata discovery and governance integration.

•Data Sets, Engineering and Features: Build governed data products, pipelines, semantic models, and engineered features with automated testing, monitoring, and lifecycle controls.

•Data Quality Dashboards and Knowledge Tools: Define quality measures and deliver role-based tools that track performance, issues, remediation, metadata, lineage, and controls.

•Natural-Language Data Access: Develop secure, traceable conversational data access using code, prompts, retrieval, query generation, guardrails, and continuous performance monitoring.

Experience and Qualifications

•Bachelor’s degree in data science, computer science, information systems, engineering, statistics, mathematics, or a related discipline; a relevant postgraduate qualification is advantageous.

•Substantial experience in data management, data governance, analytics, data engineering, or a related field, including experience leading teams or complex cross-functional delivery.

•Experience building data quality dashboards, metadata portals, knowledge tools, or comparable monitoring and self-service solutions.

•Experience implementing natural-language-to-data or conversational analytics capabilities, including coding, prompt design, retrieval, semantic modelling, query generation, validation, and guardrails.

•Proficiency in SQL and at least one relevant programming language, preferably Python; experience with business intelligence, data platform, version-control, orchestration, and cloud technologies is expected.

•Good understanding of data quality, metadata, master and reference data, privacy, security, access control, model risk, responsible AI, and records-management principles.

•Experience in a regulated financial-services environment is strongly preferred.

Leadership and Behavioural Competencies

•Ability to translate governance objectives and business needs into practical technical deliverables.

•Excellent written communication, minute writing, facilitation, presentation, and senior-stakeholder management skills.

•Strong analytical judgement, attention to detail, intellectual curiosity, and a disciplined approach to controls and evidence.

•Ability to challenge constructively, resolve ambiguity, build consensus, and escalate issues appropriately.

•Commitment to ethical, secure, and responsible use of data and AI.

Indicative Success Measures

•Timely and accurate DWMC agendas, papers, minutes, decisions, action tracking, and escalations.

•Percentage of priority data flows and Critical Data Elements with validated end-to-end lineage, ownership, definitions, and controls.

•Availability, reliability, documentation, and reuse of governed analytical data sets and features.

•Coverage and effectiveness of automated data quality rules, monitoring, dashboards, alerts, and remediation workflows.

•Reduction in unresolved data quality issues, overdue actions, and time required to assess change impacts or trace data.

•Accuracy, security, adoption, and user satisfaction for natural-language data discovery and query capabilities.

•Demonstrable compliance with access, privacy, security, model-risk, and responsible-AI requirements.

Reporting Line and Key Relationships

The role reports to the Head of Data and AI, leads the Data Management team, and works closely with Data Owners, Data Stewards, business and analytics teams, Technology, Operations, Risk Management, Legal, Compliance, Data Protection, and Information Security.