Senior Technical Product Manager – Search, Metadata & Enrichment

Role overview

Lead product work for enterprise data and document search, with a mix of release quality ownership and hands-on metadata and enrichment operations. You will connect user needs, engineering delivery, and data stewardship across a complex platform, while helping shape search and AI capabilities as the product evolves.

Responsibilities - Plan and execute release testing and user acceptance testing; trace approved requirements through delivery and flag gaps, regressions, or scope changes before launch. - Investigate metadata, tagging, classification, and data quality issues, including work that is not fully scoped in advance. - Maintain catalog metadata such as ownership, descriptions, lineage, glossary terms, and quality annotations. - Monitor search performance, diagnose missing or stale results and relevance problems, and work with engineering on indexing and ranking improvements. - Assess downstream effects across data sources, schemas, APIs, and pipelines; support platform health, incident triage, and incoming technical requests. - Contribute to roadmap and backlog decisions, keep requirements and documentation current, and communicate new functionality to users.

Requirements - At least 8 years of product management experience, preferably including hands-on data or platform operations. - Strong requirements writing, test planning, QA, and UAT experience; confident use of Jira and Confluence. - Experience with enterprise search tools such as Elasticsearch, OpenSearch, or Solr, including indexing, facets, and relevance troubleshooting. - Practical experience maintaining metadata in a management platform, including lineage, ownership, glossary, and data quality information. - Ability to reason about cross-system impacts and independently resolve ambiguous enrichment or metadata issues. - Familiarity with data enrichment methods such as entity extraction, classification, record linkage, and quality scoring; experience in pharmaceutical or regulated environments is also sought.

Nice to have

Knowledge graphs, RDF/OWL/SPARQL or Cypher, ontologies, scientific data catalogs, GCP data services, or work bridging scientific users and engineering teams are useful. Experience identifying systemic data quality problems and applying generative AI tools to documentation or product operations is also valued.