Mphasis
Role description
Job Description
Location :
New York
US
Data Engineer - Knowledge Graph & Ontology Specialist
Job Summary -
We are seeking a highly motivated and skilled Data Engineer to join our growing data team. In this role, you will be responsible for designing, developing, and implementing knowledge graphs using semantic web technologies and ontologies. You will work closely with data scientists, domain experts, and other engineers to build robust and scalable knowledge graph solutions that power various applications, including data discovery, reasoning, and advanced analytics. The ideal candidate possesses a strong understanding of data modeling principles, semantic web standards, and experience with graph databases and ontology development. You will be instrumental in shaping our data strategy and enabling data-driven decision-making across the organization.
Years of experience needed
- 3+ years of experience in data engineering or a related role.
Technical Skill
: Design, develop, and maintain knowledge graphs using semantic web technologies such as RDF, OWL, and SPARQL. Develop and implement ontologies to represent domain knowledge and relationships between entities. Extract, transform, and load (ETL) data from various sources into the knowledge graph. Develop and maintain data pipelines for continuous data ingestion and updates. Optimize graph database performance for query execution and data retrieval. Collaborate with data scientists and domain experts to understand data requirements and translate them into knowledge graph solutions. Implement data quality checks and validation rules to ensure data accuracy and consistency within the knowledge graph. Document data models, ontologies, and data pipelines. Stay up-to-date with the latest advancements in knowledge graph technologies and semantic web standards. Contribute to the development of best practices for knowledge graph development and maintenance. Work with cloud infrastructure to deploy and manage knowledge graph solutions.
Mandatory Skills: Bachelor's or Master's degree in Computer Science, Data Science, or a related field. 3+ years of experience in data engineering or a related role. Strong understanding of data modeling principles and database design. Proficiency in semantic web technologies such as RDF, OWL, and SPARQL. Experience with graph databases such as Neo4j, Amazon Neptune, or similar. Proficiency in at least one programming language such as Python, Java, or Scala. Experience with data ETL processes and tools. Experience with cloud platforms such as AWS, Azure, or GCP. Strong problem-solving and analytical skills. Excellent communication and collaboration skills.
Good-to-Have Skills: Experience with knowledge graph embedding techniques. Experience with reasoning engines and rule-based systems. Experience with ontology development and management using tools like Protégé. Experience with natural language processing (NLP) and information extraction. Experience with data governance and data quality management. Experience with DevOps practices and tools. Familiarity with linked data principles and standards. Experience with containerization technologies like Docker and Kubernetes. Experience with data visualization tools.
Certifications Needed: Education: Bachelor's or Master's degree in Computer Science, Data Science, or a related field.
About Mphasis
Mphasis applies next-generation technology to help enterprises transform businesses globally. Customer centricity is foundational to Mphasis and is reflected in the Mphasis' Front2Back Transformation approach. Front2Back uses the exponential power of cloud and cognitive to provide hyper-personalized (C=X2C2TM=1) digital experience to clients and their end customers. Mphasis' Service Transformation approach helps 'shrink the core' through the application of digital technologies across legacy environments within an enterprise, enabling businesses to stay ahead in a changing world. Mphasis' core reference architectures and tools, speed and innovation with domain expertise and specialization are key to building strong relationships with marquee clients.
Job Description
Location :
New York
US
Data Engineer - Knowledge Graph & Ontology Specialist
Job Summary -
We are seeking a highly motivated and skilled Data Engineer to join our growing data team. In this role, you will be responsible for designing, developing, and implementing knowledge graphs using semantic web technologies and ontologies. You will work closely with data scientists, domain experts, and other engineers to build robust and scalable knowledge graph solutions that power various applications, including data discovery, reasoning, and advanced analytics. The ideal candidate possesses a strong understanding of data modeling principles, semantic web standards, and experience with graph databases and ontology development. You will be instrumental in shaping our data strategy and enabling data-driven decision-making across the organization.
Years of experience needed
- 3+ years of experience in data engineering or a related role.
Technical Skill
: Design, develop, and maintain knowledge graphs using semantic web technologies such as RDF, OWL, and SPARQL. Develop and implement ontologies to represent domain knowledge and relationships between entities. Extract, transform, and load (ETL) data from various sources into the knowledge graph. Develop and maintain data pipelines for continuous data ingestion and updates. Optimize graph database performance for query execution and data retrieval. Collaborate with data scientists and domain experts to understand data requirements and translate them into knowledge graph solutions. Implement data quality checks and validation rules to ensure data accuracy and consistency within the knowledge graph. Document data models, ontologies, and data pipelines. Stay up-to-date with the latest advancements in knowledge graph technologies and semantic web standards. Contribute to the development of best practices for knowledge graph development and maintenance. Work with cloud infrastructure to deploy and manage knowledge graph solutions.
Mandatory Skills: Bachelor's or Master's degree in Computer Science, Data Science, or a related field. 3+ years of experience in data engineering or a related role. Strong understanding of data modeling principles and database design. Proficiency in semantic web technologies such as RDF, OWL, and SPARQL. Experience with graph databases such as Neo4j, Amazon Neptune, or similar. Proficiency in at least one programming language such as Python, Java, or Scala. Experience with data ETL processes and tools. Experience with cloud platforms such as AWS, Azure, or GCP. Strong problem-solving and analytical skills. Excellent communication and collaboration skills.
Good-to-Have Skills: Experience with knowledge graph embedding techniques. Experience with reasoning engines and rule-based systems. Experience with ontology development and management using tools like Protégé. Experience with natural language processing (NLP) and information extraction. Experience with data governance and data quality management. Experience with DevOps practices and tools. Familiarity with linked data principles and standards. Experience with containerization technologies like Docker and Kubernetes. Experience with data visualization tools.
Certifications Needed: Education: Bachelor's or Master's degree in Computer Science, Data Science, or a related field.
About Mphasis
Mphasis applies next-generation technology to help enterprises transform businesses globally. Customer centricity is foundational to Mphasis and is reflected in the Mphasis' Front2Back Transformation approach. Front2Back uses the exponential power of cloud and cognitive to provide hyper-personalized (C=X2C2TM=1) digital experience to clients and their end customers. Mphasis' Service Transformation approach helps 'shrink the core' through the application of digital technologies across legacy environments within an enterprise, enabling businesses to stay ahead in a changing world. Mphasis' core reference architectures and tools, speed and innovation with domain expertise and specialization are key to building strong relationships with marquee clients.