Senior Data Management Lead Consultant

Role Purpose

We are looking for a Senior Lead Data Management Consultant to lead the implementation of an enterprise data strategy across priority data domains. The role will establish the governance, architecture, metadata, modeling, semantic-layer, and data quality foundations required to create trusted, governed, reusable, and AI‑ready data assets.

This is a senior delivery role for someone who can operate across business, technology, architecture, compliance, risk, and operations. The consultant will translate strategic objectives into practical implementation plans, define clear ownership models, and drive tangible outputs. A key part of the role will be shaping the use of Denodo as the logical data access and distribution layer, defining how governed data should be structured, accessed, reused, and consumed through semantic and reusable data views.

• The consultant will lead discovery and assessment across priority data domains, identifying critical source systems, data flows, business processes, consumers, reporting needs, integration points, and dependencies across business and IT teams.
• They will define domain‑level governance structures, including data ownership, stewardship, custody, accountability, decision rights, escalation paths, and RACI models. The role requires strong judgment in clarifying unclear ownership, resolving overlaps, and aligning stakeholders on practical responsibilities.
• The consultant will drive the identification of golden sources, system positioning, trusted data assets, and authoritative datasets. This includes assessing which systems should act as sources of record, systems of reference, operational sources, distribution layers, or analytical consumption layers.
• They will lead conceptual and logical data modeling to support reusable, governed Denovo views. The models should reduce duplication, improve consistency, and create a common understanding of key entities, attributes, relationships, and definitions.
• The role will define metadata, business glossary, tagging, semantic‑layer, and data catalog requirements. Business definitions, technical metadata, ownership, lineage, sensitivity, quality rules, and usage context must be captured to support discoverability, governance, compliance, and reuse.
• They will oversee data quality assessments and support rules, controls, dashboards, monitoring, remediation workflows, and issue‑management processes. Security, privacy, classification, access control, segregation of duties, and regulatory expectations must be embedded into the target framework.
• The consultant will facilitate workshops with senior stakeholders across business, operations, technology, architecture, compliance, risk, and data teams. They must structure discussions, challenge vague requirements, manage conflicting priorities, and convert workshop outcomes into decisions and actions.
• They will translate the data strategy into a practical roadmap, including priorities, sequencing, dependencies, risks, domain‑by‑domain delivery plans, and measurable outcomes. The role also includes mentoring junior team members and providing quality assurance.

• The ideal candidate will have 8+ years of experience in data management, data governance, enterprise data architecture, data quality, metadata management, or large‑scale data transformation programs.
• Experience in regulated environments is required, ideally within banking, private banking, financial services, insurance, or another controlled industry where ownership, traceability, security, and compliance are critical.
• The candidate must have proven experience defining and implementing data governance frameworks, metadata and cataloguing approaches, business glossaries, conceptual and logical data models, data quality frameworks, and operating models for enterprise data management.
• They should understand ownership, stewardship, custody, domain governance, data accountability, golden sources, lineage, business definitions, data classification, policy alignment, and issue remediation.
• A strong understanding of data virtualization is required, including the role of Denovo in enterprise data…