Master Data Management (MDM) Technical Lead in Cambridge

Job Description Summary

GEV is building Master Data Management capabilities across Customer, Supplier, and Legal Entity first and then continue to expand to other domains that provides business impact. The MDM Technical Lead is the hands‑on owner for all the technical components, designing and building platform capabilities and leading technical execution while evolving the architecture. This is a role that leads by doing, sets the technical standard, and translates an ambitious Lean Roadmap into working, measurable deliverables.

MDM Context

MDM Context: Customer and Supplier are managed today in separate onboarding systems (each the authoritative master for its domain), with a command center (work‑in‑progress) serving data‑quality dashboards and unified view. The near‑term strategy is first to master each domain in place, explore impactful operational data domains (to master) while building the governance and data‑quality foundation. Second, make an MDM platform decision at a deliberate subsequent gate. The Technical Lead is central to both.

Job Description

In this role, you will:

Leadership & Influence

• Serve as the hands‑on technical owner for the MDM program.
• Influence and drive alignment across teams (business, DT, procurement, and domain teams).
• Mentor engineers and data stewards; raise the technical bar across the program.
• Provide the governance council and data owners with technical options, trade‑offs, and feasibility input to shape decisions.
• Lead the technical build‑vs‑buy evaluation and vendor/ proof‑of‑concept assessment, guiding leadership to a make the right decision.

Strategy & Prioritization

• Own the MDM reference architecture and its multi‑year evolution - Registry → Consolidation → Coexistence → Centralized, with explicit entry/exit criteria for each stage.
• Sequence the roadmap for incremental value: master‑in‑place now, platform decision at a deliberate gate, integrating with downstream systems.
• Prioritize domains and capabilities: Legal Entity backbone and high value domains Customer, Supplier and Operations.
• Define the canonical/logical data models, golden‑record survivorship strategy, and cross‑domain resolution for Legal Entity, Customer, and Supplier.
• Maintain deep, current knowledge of AI‑enabled / AI‑centric MDM platform architecture; shape where AI augments mastering, with guardrails for explainability, human‑in‑the‑loop review, and auditability.
• Set non‑functional requirements such as scale, performance, availability, security, lineage, and data privacy.

Adoption & Enablement

• Lead adoption steward tooling, technical documentation, and hands‑on training and enablement.
• Win stakeholder buy‑in with working prototypes and demos.
• Push data quality checks into the onboarding systems (validation at entry) so best practice is built into daily workflows.
• Build prescriptive remediation that makes stewards effective, identifying the specific defect and rule, recommending the fix, and routing the task.
• Automate survivorship, remediation routing, and orchestration to cut manual effort and rework.
• Adopt scalable architecture, match‑and‑merge engines, pipelines, and AI agent integrations.

Customer Engagement & Experience

• Engage the business consumers of master data (Customer, Supplier, and Legal Entity stakeholders) to capture requirements and to ensure that the golden record is used consistently.
• Integrate the command center with the existing Customer and Supplier onboarding systems to improve the onboarding experience and reduce cycle time.
• Deliver trusted Customer 360 and Supplier records that improve downstream experiences across CRM/sales, procurement, service, and compliance.
• Partner with domain owners and stewards as internal customers, iterating on data products based on their feedback.

Measurement & Insights

• Design and build the DQ engine, profiling, rule authoring and execution, scoring, and dashboarding on top of the existing metrics.
• Establish baselines, targets, and owners across the six DQ dimensions (completeness, uniqueness, validity, accuracy, consistency, timeliness).
• Track key KPIs: duplicate rate, match/merge accuracy, onboarding cycle time, % records with a golden record, and issue‑resolution time.
• Surface insights and quantify business outcomes (duplicate‑payment reduction, Customer‑360 completeness, spend/risk visibility).
• Convert cleanup logic into a versioned, measurable DQ rules library so improvement is trackable over time.

Leadership Reporting & Governance

• Provide regular, clear technical leadership updates to senior stakeholders; turn complex trade‑offs into executive‑ready decisions.
• Translate governance p