Description

What you'll do

AI Capability Assessment & Business Alignment

• Provides inputs to the overall Opportunity Assessment specifically for Gen AI, embedding, and large language model (LLM) product capabilities.
• Identify AI-driven customer needs by partnering with Business Product Managers and internal stakeholders, focusing on opportunities for intelligent automation, content generation, and enhanced search systems.
• Participate in the evaluation of the competitive landscape, market trends, and industry research (e.g., foundation model advancements) to articulate high-potential AI product capability opportunities.
• Conduct market validation, testing Gen AI concepts and prototypes with internal users.

Data Capability Assessment & Stakeholder Needs

• Provides inputs to the overall Opportunity Assessment specifically for entity resolution, master data management, and data quality platform capabilities.
• Identify foundational data needs by partnering with Data Science, Analytics, Technology, and Business Product teams.
• Participate in the evaluation of vendor solutions and competitive landscapes for data linking, fuzzy matching, and graph database technologies.
• Conduct validation of new data sources and ER methodologies to ensure they meet the quality, privacy, and performance thresholds required by the business.

Platform Vision & Data Strategy

• Provides inputs in product strategy and planning sessions, specifically outlining the strategic role of the foundational data platform in driving accurate identity and entity-centric data views.
• Understand and communicate the data platform vision, connecting it to the larger strategy for data governance, quality, and enterprise data assets.
• Provides inputs to connect execution KPIs (e.g., entity link rate, match confidence score, data latency) with strategic business goals.

Product Definition & Platform Roadmap

• Define the product backlog for the entity resolution platform, prioritizing features like data ingestion pipelines, standardization rules, matching algorithms, and resolution services.
• Supports the development of business cases for platform enhancements, defining scope, projected ROI (e.g., reduced data errors, increased match accuracy), capacity planning, and resource needs.
• Define and maintain the platform roadmap, ensuring dependencies on infrastructure, data governance, and privacy teams are clearly documented.
• Provides inputs to determine delivery increments that balance architectural robustness with speed-to-market for critical data capabilities.

Platform Delivery & Data Engineering Collaboration

• Provide technical requirements for platform development, including specifications for data schemas, APIs, data ingestion mechanisms (e.g., streaming/batch), and entity linking logic.
• Articulate and document how specific data requirements connect to the business 'WHY' (e.g., "This new linkage feature is necessary to unify customer records across systems X, Y, and Z").
• Collaborate closely with Data Engineering, Data Architecture, and Datascience teams to identify and document technical trade-offs regarding storage, computation, scale, and performance.
• Work with Legal & Compliance and Data Governance teams to ensure the platform adheres to data residency, privacy, and lineage requirements.
• Collect and process feedback from internal users (Data Scientists, Analysts) to rapidly iterate on platform features and data quality improvements.

Growth & Portfolio Management

• Monitor platform adoption and data usage patterns across various internal teams.
• Track, monitor, and ensure effective performance of the platform, focusing specifically on data quality metrics (completeness, accuracy) and operational costs.

Collect, analyze, and interpret performance data to drive platform lifecycle decisions, including technology upgrades, data source integration, and sunset planning for deprecated data services.