Semantic Engineer | Staff or Non-line Manager | IT Enterprise Data & Platforms Japan (Shinagawa, Japan, Tokyo)
About this opportunity
The Position
Our IT EDP Data & Analytics team is seeking an experienced Semantic Engineer to design and implement semantic models, ontologies, taxonomies, and knowledge graphs that enable AI-driven solutions across the enterprise. The successful candidate will establish semantic foundations that improve data discoverability, interoperability, governance, and AI consumption for commercial business domain.
Boehringer Ingelheim leverages Semantic Web standards and knowledge graph technologies to connect enterprise data, metadata, and business knowledge, enabling trusted AI and advanced analytics solutions.
Tasks and Responsibilities
Design, develop, and govern enterprise ontologies, taxonomies, and knowledge graphs.
Create semantic models that support AI, GenAI, semantic search, and Retrieval Augmented Generation (RAG) use cases.
Partner with business stakeholders, data architects, and domain experts to define core business concepts, relationships, and metadata standards.
Evaluate, adopt, and extend existing industry ontologies and controlled vocabularies where appropriate.
Define semantic governance standards, modeling best practices, and enterprise-wide information architecture guidelines.
Enable integration and harmonization of structured and unstructured data through semantic technologies.
Collaborate with Data Engineering, Analytics, and AI teams to ensure semantic assets are reusable across platforms and applications.
Drive adoption of semantic technologies through coaching, standards, and governance processes.
Support AI initiatives by developing trusted semantic layers that improve explainability, relevance, and contextual understanding.
Requirements
Degree in Computer Science, Engineering, Data/Information Systems, or a related field, with 3+ years of experience in software engineering, data engineering, analytics engineering, or similar roles.
Proven experience with Knowledge Graphs, Ontologies, Taxonomies, or Semantic Data Models in an enterprise environment.
Strong understanding of Semantic Web technologies including RDF, OWL, SPARQL, SKOS and SHACL.
Experience designing semantic architecture that supports knowledge discovery, metadata management, and data integration.
Experience with Snowflake Semantic Models and Open Semantic Interoperability (OSI) compliant with YAML specifications.
Experience with metaphactory, Stardog.
Experience integrating multiple enterprise data sources and business vocabularies.
Ability to translate business concepts into reusable semantic models.
Demonstrated ability to work independently, manage priorities, and proactively drive work forward in a dynamic environment.
Strong stakeholder management, analytical thinking, and structured problem‑solving skills.
Excellent communication skills in English and Japanese, enabling clear interaction with technical and non‑technical stakeholders.
Nice to have
Experience with regulated environments (e.g., GxP), validation, audit readiness, or privacy‑by‑design implementation.
Knowledge of Retrieval Augmented Generation (RAG) and Graph-RAG architectures.
Experience with vector search, semantic search, and metadata-driven AI applications.
Exposure to domain data in pharmaceutical commercial areas.
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Job details
How this role compares
Computed from every other active Data & Digital role in our database, not just this employer's listings.
We currently track 257 comparable Manager Data & Digital roles across 54 biopharma companies.
Salary context
124 of 257 peers report a salary range (USD, annualized)
Peers share this role's job function and a matching or adjacent seniority level -- not necessarily the same therapeutic area or country.
Where these roles are based
Top locations among the 257 comparable roles
+ 22 more countries
Seniority mix
257 of 257 peers have a known seniority level
Therapeutic area mix
17 of 257 peers have a known therapeutic area; the rest are genuinely unlabeled, not hidden
Similar opportunities
The closest matches from our peer group, ranked by how similar they are, not how well you'd qualify for them -- treat this as market context, not a guaranteed shortlist; a weak match is labeled as one below.
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How we calculate "similar"
No black box, no LLM guesswork: a deterministic score built from four normalized attributes. Here's this role's own peer group at different match levels, so you can see the mechanism, not just the result.
Every comparison starts from the same 100-point budget: 25 for working in the same function, 40 for the same therapeutic area, 20 for the same or adjacent seniority, 15 for the same country. A dimension we can't confirm on both sides contributes nothing, never a guess, never a free pass.
0 points, never a partial guess. A role we know almost nothing about beyond its function bottoms out at 25%; it never inflates to 100% just because there's little to compare against. Seniority uses a defined ladder (Associate → Manager → Associate Director → Senior → Principal → Director → Senior Director → Executive/VP) so "Director" and "Senior Director" count as adjacent, but "Director" and "Executive/VP" do not.