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LATAM

Senior Technical Product Manager – Search, Metadata & Enrichment

Posted 23 minutes ago
5-10 years experience
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AI Summary

The Senior Technical Product Manager will oversee release quality, search relevance, and metadata curation for a global pharmaceutical R&D data platform. They will collaborate with engineering and data stewards to drive platform improvements and integrate AI-driven search and enrichment capabilities.

About the role

We're looking for a Senior Technical Product Manager who can operate in two registers at once: hands-on, in-the-weeds execution (release testing, requirements-to-release traceability, enrichment and metadata upkeep) and senior technical judgment (navigating a complex, multi-system data landscape, anticipating downstream impact, and making sound calls where guidance is incomplete). You'll work closely with the product lead, engineering, and data stewards in a hybrid delivery model combining PDLC-style governance with agile execution — and you'll have real opportunities to shape how AI gets applied to search, enrichment, and documentation as the platform matures

 

Location: Remote, US East-Coast preferred

Time Zone Availability: EST preferred, with overlap to PST and UK time zone

Experience: 5-8+ Years of Product Management Experience

 

What You'll Do

Release & Requirements Integrity

  • Represent the voice of the customer to ensure end delivery provides desired value
  • Execute test plans and UAT ahead of every release against the Data Store / Document Store
  • Verify that requirements drafted in an approved Product Development Plan (PDP) actually show up, correctly, in the shipped release, owning requirements traceability from PDP → Jira story → release
  • Flag gaps, regressions, or scope drift before launch; support go/no-go release decisions
  • Identify systemic patterns in release defects or requirements drift and propose process fixes, not just individual fixes

Search, Enrichment & Metadata Operations

  • Take ownership of the hands-on, not-yet-defined work in the enrichment pipeline — triaging tagging/classification issues, chasing down metadata gaps, data quality cleanup — and see it through without needing a fully-scoped brief
  • Enable the maintenance and curation of metadata in the metadata management system (ownership, descriptions, glossary terms, lineage, data quality annotations) so assets stay discoverable and trustworthy
  • Monitor and triage search quality issues (missing results, poor relevance, indexing gaps, stale content) in partnership with engineering
  • Drive ongoing improvement of search relevance/ranking as the underlying content and enrichment pipelines evolve; bring informed opinions on indexing strategy andquery/relevance tuning, not just bug triage
  • Spot and escalate systemic metadata/enrichment quality issues across the catalog, not only the ones reported ad hoc

Platform & Technical Depth

  • Partner with engineering on day-to-day health, uptime, and incident triage for the Data Store and Document Store
  • Navigate a complex, multi-system data landscape (multiple sources, schemas, and governance regimes) and reason about cross-system impact before changes ship
  • Understand enough of the underlying architecture (APIs, data flows, pipelines) to have credible technical conversations with engineers and data scientists, not just relay requirements
  • Monitor support channels, triage incoming technical requests, and route them appropriately

Stakeholder, Strategy & Backlog Support

  • Serve as a point of contact for researchers and end-users reporting data/search/enrichment issues
  • Contribute to roadmap and prioritization discussions for the Data Store/Document Store, bringing an informed point of view on trade-offs, not just capturing requirements
  • Maintain backlog hygiene, documentation, and requirements traceability in Jira and Confluence
  • Participate in stand-ups, retrospectives, and release reviews
  • Produce communication materials to announce functionality to users and ensure their success
  • Support technical evaluations of other consultants when required, contributing to the assessment of skills and alignment with project needs

What You Bring

  • 8+ years of product management experience, ideally including hands-on platform/data operations experience.
  • Strong requirements documentation, QA/UAT, and test-plan execution skills
  • Expert user of Jira and Confluence for backlog management, traceability, and documentation
  • Working experience with an enterprise search / information retrieval stack (Elasticsearch, OpenSearch, Solr, or similar) — indexing, relevance tuning, faceted search, troubleshooting query/ranking issues
  • Hands-on experience with metadata management platform — managing lineage, ownership, glossary terms, and data quality annotations
  • Demonstrated ability to operate in complex, multi-system technical landscapes and reason about cross-system/downstream impact
  • Comfort with ambiguity — able to pick up undefined enrichment or metadata problems and drive them to resolution without a fully-scoped brief
  • Understanding of data enrichment concepts: entity extraction/NER, classification/tagging, record linkage, data quality scoring
  • Experience in pharmaceutical or regulated industry environments (GxP, 21 CFR Part 11)

Nice to have:

  • Knowledge graph experience is a strong bonus — familiarity with RDF/OWL/SPARQL or property-graph query languages (e.g., Cypher), ontologies/taxonomies, or controlled vocabularies (MeSH, ChEBI, UniProt, internal  glossaries)
  • Familiarity with GCP services (BigQuery, Cloud Run, GCS) relevant to the data/search stack
  • Prior experience managing or contributing to a scientific/technical data catalog at enterprise scale
  • Background bridging scientific users (chemists, biologists, computational scientists) and engineering teams
  • Track record of spotting and resolving systemic data quality issues, not just symptomatic fixes
  • Exposure to applying GenAI/LLM tooling (e.g., Gemini, Atlassian Intelligence, or similar) for documentation generation, requirements automation, or intelligent backlog prioritization

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