Lead Product Performance Data Analyst, Vantage Data Centers

• 4–7 years of experience in data analysis, analytics engineering, data management, project controls analytics, commercial analytics, or capital-project data. Exceptional candidates with fewer years may be considered where they demonstrate strong SQL, Power Query/Power BI, data modeling, validation, documentation, and controlled data maintenance, with relevant exposure to cost, schedule, or project data
• Advanced analytical foundation in Excel and Power Query, SQL and Power BI or equivalent tools
• Demonstrated data management ability to structure, validate, reconcile, and document complex datasets from multiple sources, including ambiguous or messy source data
• Strong understanding of data modeling concepts and practices, including schemas, keys, reference data, hierarchy, and lineage
• Experience maintaining controlled data structures including shared coding, mappings, reference tables, and hierarchy management
• Experience building repeatable data quality and validation regimes including QA checks, reconciliation, audit logs, refresh protocols, and exception reporting. Demonstrated business data reasoning and judgement including the ability to identify inconsistencies, explain variances, and separate signals from data-quality noise
• Strong communication and documentation discipline: ability to clearly explain and document assumptions, explain confidence levels, and support stakeholders using the data or reports, with attention to data quality
• Don't meet all the requirements? Please still apply if you think you are the right person for the position

Desirable

• The Product team at Vantage defines and stewards our global product platform: the customer outcomes we aim to deliver, the standards and reference approaches that make delivery repeatable, and the closed-loop learning that turns each deployment into an upgrade for the next
• We do this in partnership with Engineering, Delivery, Operations, Procurement, Sustainability, Sales, Site Selection, and Business Development, aligning priorities and trade-offs while preserving clear functional ownership for design execution, construction delivery, and site operations
• Vantage is building a repeatable, product-led approach to delivering hyperscale infrastructure and operating data centers on a global scale
• We are hiring a Product Performance Data Analyst to own the controlled data foundation behind that approach: the integrated cost, schedule, and benchmark data structures on which our baselines, variance analysis, and executive reporting are built. This person will help build the trusted data layer behind how Vantage measures, compares, and improves its global data center product platform
• This is a hands-on, high-leverage product data and analytics role for a skilled analyst who wants to turn complex cost, schedule, and project data into the trusted decision layer for Vantage products. It combines advanced data management, analytics, business judgement, and industry benchmarks to change the way Vantage builds infrastructure
• You will shape the performance data model for Vantage’s global infrastructure products and maintain the controlled data foundation behind Product Performance: the integrated WBS, cost, schedule, and benchmark data structures; source metadata, mappings, and refresh controls; validation and reconciliation routines; and the repeatable reporting built on top
• You will keep the foundation auditable, refreshable, and capable of supporting product performance analysis over time, and you will work with Developers and IT to progressively tool and automate it
• Your work will be directly used by product managers, engineering, procurement, solutions, construction delivery, and operations executives to drive decisions on product direction, priorities, and customer experience
• Define, build, and maintain a targeted set of common KPIs and analytics views for Vantage leadership to evaluate product performance, compare with baselines, and make decisions
• Maintain benchmark datasets, structured workbooks, schemas, data dictionaries, stable identifiers, and the integrated WBS, cost, and schedule hierarchy
• Validate and rationalize digital supplier, consultant, and internal submissions against required fields, permitted values, and naming standards
• Run QA checks for duplicates, missing metadata, broken mappings, orphan records, version issues, and reference integrity failures
• Maintain source grade, confidence level, base date, currency, escalation basis, assumptions, and ownership metadata
• Reconcile maintained data against RFPs, general contractor estimates, consultant data, internal delivery actuals, and market evidence
• Support variance analysis by maintaining clear, comparable baseline and reference-point data, and by flagging data movements, gaps, or inconsistencies that require review
• Support the closed feedback loop from project data and benchmark refreshes back into product performance decisions by ensuring lessons from validated outcomes are captured in the governed data foundation
• Maintain regional factor-pack inputs and support configuration bridge analysis, sensitivity reporting, and regional variant data maintenance
• Maintain change logs, assumptions logs, validation reports, and refresh-cycle records, and provide analytical input to benchmark refresh decisions
• Populate, validate, and reconcile baseline refresh inputs against approved source data, assumptions, and reference points
• Gather, normalize, and reconcile internal cost, schedule, and change data to bridge project baseline, approved business case, and estimate-at-completion reference points
• Support variance analysis explaining movements between baselines and reference points, clearly separating data-quality issues from genuine performance movements
• Support techno-economic analysis for new products or solutions in partnership with product managers and engineers
• Build and maintain Power BI dashboards and analytical views across baseline and reference points, supporting multiple reporting levels
• Assist developers and IT with tooling the dataset by translating data requirements, field definitions, validation rules, and reporting needs into build-ready requirements, and automate validation, reporting, and import routines over time
• What Success Looks Like:
• Stakeholders use the dataset with confidence because validation, reconciliation, and metadata make its quality visible and its gaps explicit
• Every number has lineage: source grade, confidence level, base date, currency, escalation basis, assumptions, and ownership are maintained as a matter of course
• Refresh cycles run as controlled processes, with change logs, validation reports, and auditable records
• Variance analysis is well served: baseline and reference-point data are clean and comparable, and data movements, gaps, and inconsistencies are flagged before they mislead a decision
• Reporting works at every level, from working analysis to leadership dashboards, from the same governed foundation
• Product teams use governed benchmark and performance data to identify, compare, and prioritize product optimization opportunities
• Product and delivery teams can self-serve routine performance questions, while custom analysis requests are evaluated through a structured and transparent process. Validation, import, and reporting routines are increasingly automated