Data & Knowledge Graph Architect

Position/TITLE: Data & Knowledge Graph ArchitectJob Description:Looking for a senior knowledge graph architect with expertise in data engineering and programming to build systems that collect, manage, and convert raw data into usable information for business requirements. As a knowledge Engineer, you'll play a crucial role in ensuring data retrieval, reliability, quality, and efficiency within the organization and data retrieval with RDF based Graph database.Minimum Experience & Mandatory Skills10-12+ yrs: Python, Java, Spark; Data Pipeline Design; SPARQL/SQL/NoSQL/Kafka with Python; Batch & Stream Processing; Large Data Handling; Performance Optimization6+ yrs: RDF based Graph Databases; Vector Databases8+ yrs: Cloud (Azure/AWS/GCP); REST APIs & Messaging; Process Automation6+ yrs of working experience in Architect roleJOB ResponsibilitiesDesign and develop RDF based Graph Databases and Knowledge graph implementation.Design complex SPARQL & SQL code development process.Implement Knowledge graph population alignment with ontologyModify or create ontologies on need basisImplement Graph indexes, data retrieval and performance optimizationAnalyze and organize raw data: Work with various data sources, parsing documents, extracting relevant information and structuring it for further processing.Build data systems and pipelines: Construct robust data pipelines that facilitate data flow from source to Target.Evaluate business needs and objectives: Understand the company's requirements and align data systems accordingly.Interpret trends and patterns: Use your analytical skills to identify data patterns.Conduct complex data analysis and report on results: Dive deep into data to extract meaningful information.Prepare data for prescriptive and predictive modeling: Ensure data is ready for machine learning and statistical analysis.Build algorithms and prototypes: Develop and test data processing algorithms.Combine raw information from different sources: Integrate data from various systems.Explore ways to enhance data quality and reliability: Continuously improve data processes.Identify opportunities for data acquisition: Stay informed about new data sources.Develop analytical tools and programs: Create tools to facilitate data analysis.Collaborate with data scientists and architects: Work closely with other data professionals to achieve common goals.Implement data access controls, data encryption, and data masking techniquesFamiliarity with data visualization tools and techniques for presenting dataCreate and maintain dashboards and reports for stakeholders.Common Mandatory Skills – Must Have:Proficiency with AI Coding Agents: Ability to leverage AI-assisted coding tools for development and problem-solving.Strong Logical Reasoning: Demonstrated capability to analyze complex problems and design efficient solutions.Adaptability to Alternative Technologies: Flexibility to learn and work on different technologies as per project requirements (training will be provided).Mandatory Skills:Strong experience with RDF Graph databases (e.g. RDF4j, Virtuoso, Graph DB, Apache Jena, etc.)Strong experience with Vector databases (e.g. Pinecone, FAISS, etc.)Strong SPARQL skillsStrong Python-Kafka skill.Design, develop, and maintain data pipelines.Exposure to process automation.Experience working with REST API and Fast API s and services, messaging and event technologies.Experience working with large and complex data sets.Hands-on experience with SQL/No-SQL database (RDS, Redshift, DynamoDB, synapse, big query, mongo, etc.)Batch/stream data processing experienceGood knowledge of programming languages (e.g., Python, Java, Spark, etc).Monitor, troubleshoot, and optimize the performance of data infrastructure to ensure scalability, reliability, and cost efficiency.Stay up to date with cloud services and best practices in data engineering to continuously improve our data ecosystem.Good exposure on at least two public cloud platforms (Azure/AWS/GCP)Good-to-Have Skills:- Knowledge or work experience in insurance, mortgage, banking domains.- Proficiency in building stream processing systems using kinesis, Kafka, etc.- Familiarity with Docker, Kubernetes, CI/CD and cloud services (AWS, Azure, GCP).- Technical expertise in segmentation techniques.- NLP knowledge