Master Data Management (MDM) Technical Lead

GE Vernova•Stamford, CT

22h

About The Position

GE Vernova 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. The MDM Technical Lead is central to both the near-term strategy of mastering each domain in place and exploring impactful operational data domains, while building the governance and data-quality foundation, and the subsequent decision to make an MDM platform decision at a deliberate subsequent gate. This role will own the architecture evolution from Registry → Consolidation → Coexistence → Centralized, bring a deep, current understanding of AI-enabled and AI-centric MDM platforms, and operate in a dynamic and evolving ecosystem, providing technical leadership updates and driving technical alignment across business functions, segments and Digital Technology teams.

Requirements

Bachelor’s degree in technology, Data, Business, or related field; or equivalent experience.
8 Years+ Experience in enablement Data capabilities for business outcomes and use of digital workplace collaboration tools.
Hands-on MDM depth: significant experience in data management, preferably with enterprise MDM platform.
Architecture-style breadth: demonstrated experience across multiple MDM styles (Registry, Consolidation, Coexistence, and/or Centralized) and the transitions between them.
Core MDM engineering: strong hands-on data modeling; match/merge and survivorship design; reference-data and hierarchy management; cross-reference (XREF).
Integration & data engineering: APIs, event/streaming, and ETL/ELT across real-time and batch patterns; strong SQL.
Data quality: profiling, rule design and execution, scoring, and remediation.
Applied AI/ML awareness: working knowledge of AI/ML applied to entity resolution and data quality, and the judgment to evaluate it critically.
Enterprise delivery: track record delivering in large, matrixed enterprises; able to provide leadership updates and drive cross-functional alignment.

Nice To Haves

Lean / Lean Six Sigma experience: process optimization, waste reduction, and value-stream thinking applied to data operations.
Production AI-enabled MDM: hands-on with AI-centric matching, stewardship, or data-quality capabilities in a live environment.
Domain depth in Customer, Supplier, and/or Legal Entity mastering; vendor-evaluation or procurement experience.
Cloud data platforms (Snowflake or Databricks).
Certifications such as DAMA CDMP or relevant cloud/architecture credentials.
Adjacent domains: experience mastering Operations Data Domains (useful for extending MDM into engineering and operations).

Responsibilities

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.
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.
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.
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.
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.
Provide regular, clear technical leadership updates to senior stakeholders; turn complex trade-offs into executive-ready decisions.
Translate governance policies into enforceable technical standards — naming, survivorship precedence, match thresholds, and reference-data control.
Stand up and configure the business glossary / data catalog and the technical foundation for stewardship workflows.
Supply the governance council and decision gates with the evidence, options, and reporting needed to govern MDM initiatives.
Ensure lineage, auditability, and compliance are built into the platform, leveraging the existing audit capability.

Benefits

medical, dental, vision, and prescription drug coverage
access to Health Coach from GE Vernova, a 24/7 nurse-based resource
access to the Employee Assistance Program, providing 24/7 confidential assessment, counseling and referral services
GE Vernova Retirement Savings Plan, a tax-advantaged 401(k) savings opportunity with company matching contributions and company retirement contributions, as well as access to Fidelity resources and financial planning consultants
tuition assistance
adoption assistance
paid parental leave
disability benefits
life insurance
12 paid holidays
permissive time off