Senior Master Data Manager at IREN in San Francisco, CA

Job description

Job Type: Full-Time l Location: San Francisco, CA l Department: AI Infrastructure Supply Chain l Reporting to: Director, Supply Chain Analytics | Work Location Type: #hybrid

IREN is a leading AI Cloud Service Provider, delivering large-scale GPU clusters for AI training and inference. IREN’s vertically integrated platform is underpinned by its expansive portfolio of grid-connected land and data centers in renewable-rich regions across the U.S. and Canada.

The Senior Master Data Manager owns the strategy, structure, and governance of master data across IREN's AI infrastructure and data center supply chain, and ensures that finance, procurement, inventory, quality, cost, project, and site data describe the same materials, suppliers, and locations in the same way. Reporting to the Director, Analytics & Data Engineering, this senior individual-contributor role defines the master data model, the standards and workflows that keep it accurate, and the integration patterns by which ERP, PLM, procurement, quality, warehouse, and project-controls systems exchange and ingest master data. The role partners closely with Finance, Technical Sourcing, Supply Chain Operations[JA1] , PLM Quality, Data Center Engineering, IT, and the Analytics & Data Engineering team so that every downstream system, report, and model is built on one trusted, connected set of master data.
[JA1]& PLM Team

With 100% renewable energy, we build, own and operate our data centers and take pride in being at the forefront of sustainable solutions for the ever-evolving applications of high performance compute. We believe that human progress is invaluable, but it should be done in the right way - responsibly, sustainably and having a positive impact on the communities we operate in.

Job requirements

• Bachelor’s degree in Information Systems, Supply Chain Management, Engineering, Computer Science, or a related field; MBA or advanced degree preferred.
• 10+ years of progressive experience in master data management, data governance, or enterprise data architecture for supply chain, manufacturing, or operations data, ideally at a hyperscaler, cloud provider, OEM/ODM, or AI infrastructure company.
• Proven experience designing and owning enterprise master data models and domains (material/part, supplier, customer, BOM, site/project, cost center, and chart-of-accounts mappings), including hierarchies, taxonomies, attributes, and lifecycle rules.
• Strong technical understanding of how master data flows between ERP, PLM, procurement/P2P, quality (QMS), inventory and warehouse, finance, and project-controls systems, and of the integration patterns (APIs, middleware, extracts, CDC, event-based) used to keep them in sync.
• Hands-on experience with MDM and data-governance tooling and with ERP master data (SAP MDG, ECC, or S/4HANA a plus but not required), including data migration, cleansing, de-duplication, and survivorship rules.
• Working expertise with data-quality measurement, data stewardship operating models, and change-control workflows for master data creation, change, and obsolescence.
• Experience leading cross-functional data design with finance, procurement, quality, and operations stakeholders, translating business definitions into system-ready structures and reconciling conflicting requirements.
• Strong hands-on problem solving, data analysis, and communication skills, with the ability to operate autonomously across finance, supply chain, quality, IT, and data engineering teams.

Other important requirements

• Pre-employment screening, including background check and substance testing, may be required according to company policies.
• Must be able to reliably commute to the assigned office location on scheduled in-office days under the hybrid work arrangement.
• This role may involve periodic travel to supplier and vendor sites, data centers, and industry events.
• IREN does not yet have an office in the Bay Area (a Santa Clara office is planned); until it opens, this role will work remotely, transitioning to hybrid on-site work once the office is established.

Job responsibilities

• Own the master data strategy and roadmap for IREN’s AI infrastructure supply chain, defining which domains are mastered, where each is sourced, and how master data is distributed to consuming systems.
• Define and decide the source-of-truth system and record for all master data, including but not limited to Part Master, Material/Item Master, Product Master, Supplier Master, Customer Master (likely the data center as the customer entity in IREN’s case), Asset Master and Service Master, and own the mapping of master data domains to their source-of-truth systems.
• Design and govern the integration architecture by which ERP, PLM, procurement, quality, inventory and warehouse, finance, and project-controls systems ingest, exchange, and reconcile master data, working with IT and Data Engineering on APIs, middleware, and data pipelines.
• Ensure that systematic linkages are in place to flow master data from the source of truth to downstream systems as needed to support the business, and that the integrity of master records is maintained and compliant over the life cycle of the record.
• Develop and lead periodic data reviews and audits in partnership with functional data owners to ensure accuracy.
• Establish master data governance policies, data ownership, stewardship roles, and approval workflows, and serve as the senior escalation point for data standards, conflicts, and exceptions.
• Define master data quality KPIs (completeness, accuracy, consistency, timeliness, duplication) and drive root-cause remediation with system owners, stewards, and Data Engineering.
• Partner with Analytics & Data Engineering to publish conformed dimensions, reference data, and a data catalog so analysts and business users can find, trust, and consistently use master data.
• Translate business definitions from Finance, Sourcing, Quality, and Operations into structured, system-ready master data standards and resolve conflicting requirements across functions.
• Identify systemic gaps in data capture, identifiers, and cross-system mappings, and lead continuous-improvement initiatives to close them.
• Publish master data health and integration status reporting for leadership, with clear risks and recommended actions.