Data Product Manager

Globality, Inc.•Palo Alto, CA

2h•$150,000 - $250,000•Onsite

About The Position

Globality is an autonomous sourcing platform that manages the intake-to-award process. Its technology interprets purchase intent, generates sourcing materials, evaluates suppliers, and executes negotiation strategies. The Data Product Manager will own the strategy for capturing, structuring, governing, and using sourcing data as a proprietary asset that improves the platform and its agents, strengthens Globality’s market position, and delivers customer insights. The role will run data as a product with its own roadmap, users, and measurable return, partnering with Engineering, Machine Learning, the AI Product Manager, Product leadership, Legal, Security, and Sales. The position also owns customer-facing analytics and benchmark products, including spend and savings reporting and customer insight dashboards. It is based in Globality’s Palo Alto office, where the team works together four days per week. The anticipated annual base salary range is $150,000–$250,000; actual compensation may vary based on experience, skills, and location.

Requirements

At least 5 years of product management experience, including at least 3 years owning data platform, data infrastructure, or AI/ML data products.
Track record of turning proprietary data into a strategic asset, such as data network effects, industry benchmarks, or training data that measurably improved model performance.
Deep understanding of data architecture, including data modeling, pipelines, warehouses and lakehouses, entity resolution, and master data management.
Experience working closely with ML teams on training data, labeling, evaluation datasets, and feedback loops for machine learning or LLM-based systems.
Experience structuring and normalizing messy, multi-source enterprise data.
Working knowledge of data rights, privacy, and governance in multi-tenant B2B SaaS, including anonymization and aggregation of customer data.
Strong technical fluency, including SQL, and ability to assess data quality and validate assumptions independently.
Strategic, business-oriented mindset and ability to articulate the economic value of data and make the case for investment to executive leadership.
Experience turning customer or operational data into analytics, dashboards, or benchmarks used by external customers.

Nice To Haves

Experience with procurement, sourcing, or spend data.

Responsibilities

Own the data product strategy and roadmap for capturing, structuring, and compounding customer and sourcing data into a proprietary asset.
Identify data with durable competitive value, including purchase intent, should-cost benchmarks, supplier pricing and performance, negotiation outcomes, and award decisions, and prioritize investment.
Design instrumentation to capture high-value signals throughout the intake-to-award process, partnering with workflow product managers and addressing missing or unstructured data.
Define canonical data models, taxonomies, and entity resolution for suppliers, categories, and commercial terms so data from customers, ERP platforms, and procurement systems can be unified and reused.
Build feedback loops that convert sourcing outcomes into training data, evaluation sets, and benchmarks to improve agent capabilities such as target pricing, supplier discovery, negotiation strategy, and award recommendations.
Partner with the AI Product Manager, Machine Learning, and Engineering to make data usable by models and agents, including labeled datasets, feature pipelines, and retrieval sources.
Define data-powered product capabilities, such as market pricing and should-cost benchmarks, in collaboration with Product leadership.
Work with Legal, Security, and Sales to establish data rights, consent, and contractual frameworks for using aggregated and anonymized data across customers.
Set and measure standards for data quality, coverage, and freshness, including category coverage, model performance lift, and customer outcomes.
Own the roadmap for customer-facing analytics, such as spend and savings reporting and customer insight dashboards, with Product leadership.
Define and consistently apply customer-result metrics, including cycle time, savings, spend under management, supplier participation, and award outcomes, and document them in a shared data dictionary.
Ensure customer data is handled in line with SOC 2 Type 2, ISO 27001:2022, GDPR, and CCPA requirements, including data segregation and access controls.