Data & Knowledge Graph Architect

Dear Applicant,

Please let me know if you are interested.

• Title: Data & Knowledge Graph Architect
• Location: Dallas , TX – 5 days onsite
• Hire Type: Long Term Contract

Job Description:

• Experience : 10- 12 Yrs
• Job 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 Skills
• 10-12+ yrs: Python, Java, Spark; Data Pipeline Design; SPARQL/SQL/NoSQL/Kafka with Python; Batch & Stream Processing; Large Data Handling; Performance Optimization
• 6+ yrs: RDF based Graph Databases; Vector Databases
• 8+ yrs: Cloud (Azure/AWS/GCP); REST APIs & Messaging; Process Automation
• 6+ yrs of working experience in Architect role
• Responsibilities:
• Design and develop RDF based Graph Databases and Knowledge graph implementation.
• Design complex SPARQL & SQL code development process.
• Implement Knowledge graph population alignment with ontology
• Modify or create ontologies on need basis
• Implement Graph indexes, data retrieval and performance optimization
• Analyze 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 techniques
• Familiarity with data visualization tools and techniques for presenting data
• Create 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 skills
• Strong 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 experience
• Good 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