Senior Technical Product Manager – Search, Metadata & Enrichment
LATAM Jyväskylä, Central Finland, Finland
IT Services and IT Consulting · 1,001-5,000 employees
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About the role
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.
What they look for
Requirements
Candidates must have 8+ years of product management experience with a strong background in data operations, enterprise search stacks, and metadata management. Proficiency in Jira, Confluence, and navigating complex, multi-system technical landscapes in regulated environments is required.
Full description
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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