Master Data Management (MDM) Technical Lead
Stamford, CT - USA
Job Summary
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.
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.
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 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.
Basic Qualifications
- Bachelors 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.
Desired Characteristics
- 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).
GE Vernova offers a great work environment professional development challenging careers and competitive compensation. GE Vernova is anEqual Opportunity Employer. Employment decisions are made without regard to race color religion national or ethnic origin sex sexual orientation gender identity or expression age disability protected veteran status or other characteristics protected by law.
GE Vernova will only employ those who are legally authorized to work in the United States for this opening. Any offer of employment is conditioned upon the successful completion of a drug screen (as applicable).
Relocation Assistance Provided: No
Required Experience:
Senior IC
About Company
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