Contribute towards defining Reference data management framework for Enterprise
Develop and optimize RDF/OWL/SKOS-based semantic frameworks and SPARQL/SQL queries.
Support semantic data integration, metadata harmonization, and semantic publishing workflows.
Manage ontology lifecycle activities including modeling, validation, versioning, and publishing.
Collaborate with multiple enterprise & functional teams to elicit, structure and formalize knowledge from domain experts and diverse sources to build CVs, taxonomies, and ontologies.
Troubleshoot semantic data load and mapping issues across Semantic Layer, CDL, and graph platforms.
Develop and optimize automated data ingestion / pipelines through Python/PySpark when APIs are available
Support enterprise Data Foundations, Knowledge Graph and Metadata Management initiatives.
Mentor junior engineers and provide technical leadership on semantic technologies.
Identify and resolve complex data-related challenges
Participate in sprint planning meetings and provide estimations on technical implementation.
Basic Qualifications and Experience
Master’s degree with 7- 10 years of experience in Business, Engineering, IT or related field OR
Bachelor’s degree with 8 - 12 years of experience in Business, Engineering, IT or related field OR
Functional Skills
Must-Have Skills
Mandatory: 8–12 years of experience in Reference Data Engineering, Semantic Technologies, or Knowledge Graph implementations.
Advanced knowledge of ontologies and taxonomies with proficiency in Semantic Web technologies, standards and tools such as RDF/s, OWL, SKOS, SPARQL, SHACL, and Linked Data standards.
Hands-on experience with GraphDBs, TopBraid EDG, CenTree, MarkLogic, Stardog, or similar semantic platforms.
Strong expertise in Pharma Domain CVs/Taxonomies/Ontologies such as CDISC, MedDRA, WHODrug, NCIT, IDMP SPOR, SNOMED CT, ICD 10/11 etc. and integrating them into enterprise-wide applications.
Strong understanding of metadata management, reference data governance, and semantic interoperability.
Hands-on experience with modern data platforms such as Databricks and cloud engineering platforms such as AWS.
Experience with APIs, JSON-LD, XML, SQL, and semantic integration frameworks.
Strong analytical, troubleshooting, and stakeholder management skills.
Familiarity with FAIR Data Principles, Semantic Layer, metadata repositories, data catalogs and semantic publishing pipelines.
Exposure to enterprise knowledge graph and AI/ML initiatives.
Experience working in Agile delivery models and cloud-based ecosystems.
Technical Skills
Semantic Technologies: RDF, OWL, SKOS, SPARQL, SHACL, Linked Data, JSON-LD
Platforms & Tools: GraphDB, TopBraid, CenTree, MarkLogic, Protégé, Semaphore, Databricks
Data & Integration: SQL, PySpark, ETL, Git, REST APIs, XML, JSON, Metadata & Data Governance
Professional Certifications
Databricks Certificate preferred
SAFe® Practitioner Certificate preferred
Any Data Analysis certification (SQL, Python)
Any cloud certification (AWS or AZURE)
Soft Skills
Strong analytical abilities to assess and improve master data processes and solutions.
Excellent verbal and written communication skills, with the ability to convey complex data concepts clearly to technical and non-technical stakeholders.
Effective problem-solving skills to address data-related issues and implement scalable solutions.
Ability to work effectively with global, virtual teams
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