6 Rdfs Jobs

Job description We at DC Varian are looking for an enthusiastic developer with following skills, roles and responsibilities Roles and Responsibilities: Responsible for the high level and low level design Implementation of features and/or bug-fixing and delivering solutions adhering to coding and quality guidelines Identification and implementation of (unit/integration) tests to ensure solution addresses customer requirements and does not cause impact to installed base Performing code review, providing documentation (requirement/design/test specification) inputs, and ensuring delivery conforms to organization and project quality processes Desired Experience: 7+ years of hands-on development experience and highly proficient in C/C++ programming 5 years of experience in embedded software development Working experience with C# is an added advantage Design and implement object-oriented software across PC and embedded programming languages (C#, C, C++) Experience with control systems (medical or industrial), and state machine-based implementation. Experience real-time operating systems such as VxWorks (preferred), QNX, or Others, Rhapsody Tool Good communication skills and be fluent in English (writing and speaking) Knowledge of DICOM and medical domain are added plus

JOB REQUIREMENTS/ SKILLS: Experience in Data Engineering and Ontology Expert in Data modelling, high level architecture designs and concepts with 2-7 years of experience Strong Python programming skills Good experience in ETL (Extract, Transform, Load) operations using Python and loading to graph DB. Expert in converting data into RDF. Expert in understanding of triple store database concepts and implementation in Graph DB (like Blazegraph) Supports new developments in Digitalization which are based on 3D, 4D and 5Ddata Expert in converting data from different formats like xml, excel etc into RDF (Resource Description Framework) Expert in various relational databases and NoSQL databases like MongoDB, semantics, triple stores etc Hands on working experience in Semantic Web standards from W3C: RDF, RDFS, OWL, SPARQL etc. Large scale data visualizations on the lines of d3js, Graphviz. Advance Level knowledge on data integration and complex data workflows Mandatory Skills: RDF, RDFS, OWL, SPARQL, Data Modeling Minimum 1 year of relevant (only hands on experience is countable, not just knowledge or awareness) experience on above mandatory technology stack Total experience should be about 2-7 years of experience

Job description Experience in Data Engineering and Ontology Expert in Data modelling, high level architecture designs and concepts with 2-7 years of experience Strong Python programming skills Good experience in ETL (Extract, Transform, Load) operations using Python and loading to graph DB. Expert in converting data into RDF. Expert in understanding of triple store database concepts and implementation in Graph DB (like Blazegraph) Supports new developments in Digitalization which are based on 3D, 4D and 5Ddata Expert in converting data from different formats like xml, excel etc into RDF (Resource Description Framework) Expert in various relational databases and NoSQL databases like MongoDB, semantics, triple stores etc Hands on working experience in Semantic Web standards from W3C: RDF, RDFS, OWL, SPARQL etc Large scale data visualizations on the lines of d3js, Graphviz. Advance Level knowledge on data integration and complex data workflows Mandatory Skills: RDF, RDFS, OWL, SPARQL, Data Modeling Minimum 1 year of relevant (only hands on experience is countable, not just knowledge or awareness) experience on above mandatory technology stack Total experience should be about 2 - 7 years of experience

Semantic Web technologies: o RDF o RDFS, OWL, SHACL o SPARQL o JSON-LD, N-Triples/N-Quads, Turtle, RDF/XML, TriX API-led architectures o REST, SOAP o Microservices o API Management Graph databases, such as Dydra, Amazon Neptune, Neo4J, Oracle Spatial & Graph is a plus Experience with other NoSQL databases, such as key-value databases and document-based databases (e.g. XML databases) is a plu Experience with relational databases Programming experience, preferably Java, JavaScript, Python, PL/SQL Experience with web technologies: HTML, CSS, XML, XSLT, XPath Experience with modelling languages such as UML Understanding of CI/CD automation, version control, build automation, testing frameworks, static code analysis, IT service management, artifact management, container management, and experience with related tools and platforms. Familiarity with Cloud computing concepts (e.g. in AWS and Azure).

Hiring for expert in Semantic Web (RDF, OWL, SPARQL), API-led architecture (REST/SOAP), Graph & NoSQL DBs, Java/Python, web tech (HTML, XML), UML, CI/CD, and cloud (AWS/Azure). Experience with Neo4j, Amazon Neptune, and microservices a plus.

The Role : The Knowledge Engineering team are seeking a Lead Knowledge Engineer to support our strategic transformation from a traditional data organization into a next generation interconnected data intelligence organization. The Team : The Knowledge Engineering team within data strategy and governance helps to lead fundamental organizational and operational change driving our linked data, open data, and data governance strategy, both internally and externally. The team partners closely with data and software engineering to envision and build the next generation of data architecture and tooling with modern technologies. The Impact : Knowledge Engineering efforts occur within the broader context of major strategic initiatives to extend market leadership and build next-generation data, insights and analytics products that are powered by our world class datasets. Whats in it for you : The Lead Knowledge Engineer role is an opportunity to work as an individual contributor in creatively solving complex challenges alongside visionary leadership and colleagues. Its a role with highly visible initiatives and outsized impact. The wider division has a great culture of innovation, collaboration, and flexibility with a focus on delivery. Every person is respected and encouraged to be their authentic self. Responsibilities : Develop, implement, and continue to enhance ontologies, taxonomies, knowledge graphs, and related semantic artefacts for interconnected data, as well as topical/indexed query, search, and asset discovery Design and prototype data software engineering solutions enabling to scale the construction, maintenance and consumption of semantic artefacts and interconnected data layer for various application contexts Provide thought leadership for strategic projects ensuring timelines are feasible, work is effectively prioritized, and deliverables met Influence the strategic semantic vision, roadmap, and next-generation architecture Execute on the interconnected data vision by creating linked metadata schemes to harmonize semantics across systems and domains Analyze and implement knowledge organization strategies using tools capable of metadata management, ontology management, and semantic enrichment Influence and participate in governance bodies to advocate for the use of established semantics and knowledge-based tools Qualifications: Able to communicate complex technical strategies and concepts in a relatable way to both technical and non-technical stakeholders and executives to effectively persuade and influence 5+ years of experience with ontology development, semantic web technologies (RDF, RDFS, OWL, SPARQL) and open-source or commercial semantic tools (e.g., VocBench, TopQuadrant, PoolParty, RDFLib, triple stores); Advanced studies in computer science, knowledge engineering, information sciences, or related discipline preferred 3+ years of experience in advanced data integration with semantic and knowledge graph technologies in complex, enterprise-class, multi-system environment(s); skilled in all phases from conceptualization to optimization Programming skills in a mainstream programming language (Python, Java, JavaScript), with experience in utilizing cloud services (AWS, Google Cloud, Azure) is a great bonus Understanding of the agile development life cycle and the broader data management discipline (data governance, data quality, metadata management, reference and master data management)