Data Strategy & Sourcing Analyst
Listed on 2026-06-08
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Business
Data Analyst -
IT/Tech
Data Analyst
is the System of Action for the Office of the CFO, transforming the way businesses purchase and pay into an intuitive, B2C-like shopping experience. leverages embedded AI agents and embedded financial products to reinvent the way businesses connect with their vendors.
End users enjoy a seamless, zero-training buying experience, while finance and procurement leaders gain a single platform to orchestrate how the business “should operate”. The result is an all-in-one solution that serves as a gravitational pull for spend and data, automating and eliminating procurement and finance workflows from requisition to reconciliation along the way.
is on the cutting edge of B2B Agentic Commerce, poised to be the market leader in creating a more predictive, prescriptive, and personalized experience for users.
Founded in 2016 and headquartered in New York City, oversees nearly half a billion in annualized spend across hundreds of customers like WeWork, Soul Cycle, Lume, and [solidcore] . has raised $75M in funding from industry-leading investors like MIT, Stage 2 Capital, Rally Ventures, 645 Ventures, and more. has been proudly named a 50 to Watch by Spend Matters and a Best Place to Work by Built In and Inc.
Magazine.
The Data Strategy and Sourcing Optimization Analyst is a critical, cross-functional role responsible for transforming raw product data into a strategic asset. This role bridges data governance and financial optimization by ensuring the creation and maintenance of a "single source of truth" (SSOT) for all product catalog and vendor data, and then leveraging that standardized data to drive significant cost savings and revenue opportunities through strategic sourcing.
The analyst will partner closely with Data, Catalog Setup, and Vendor Operations to align data integrity with core business objectives.
- Establish and maintain the Single Source of Truth (SSOT) for product catalog data by defining, documenting, and enforcing organizational data standards (e.g., attribute definitions, classification, and naming conventions).
- Integrate consolidated product, vendor, and transactional data to build a comprehensive data model specifically designed to support advanced sourcing analysis, pricing comparisons, and substitution logic.
- Conduct forensic data analysis across disparate systems to identify, prioritize, and resolve data quality issues (duplicates, inaccuracies) that impede effective sourcing, negotiation, and substitution efforts.
- Proactively analyze SSOT data to uncover strategic sourcing and cost-reduction opportunities, focusing on identifying vendor pricing gaps, suboptimal product utilization, and opportunities for vendor rationalization.
- Translate data insights into actionable financial strategies by collaborating with Vendor Operations and Customer Success teams to validate identified opportunities, inform vendor negotiations, and drive successful product substitutions.
- Monitor the financial impact and success metrics of product substitution rates, cost savings achieved, and overall data quality improvement.
- Provide data-driven recommendations on product lifecycle management and inventory efficiency based on the SSOT metrics.
- Serve as the data champion and primary liaison between teams on all matters related to product and pricing data integrity and utilization.
- Lead cross-functional working groups to achieve consensus on data definitions, resolve conflicting requirements, and standardize data input and usage across departments.
Education and Experience
- Bachelor's degree in a related quantitative field.
- 1-2 years of progressive experience in data management, data quality, or a combined role focusing on data consolidation and procurement/supply chain optimization.
- Experience working with Product Information Management (PIM), Master Data Management (MDM), or Enterprise Resource Planning (ERP) systems.
- Expert proficiency in SQL for complex querying, analysis, and joining of product, vendor, and transactional datasets.
- Strong practical…
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