Architect Advanced

Job 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.
• Create and maintain dashboards and reports for stakeholders.

Mandatory Skills

• Familiarity with data visualization tools and techniques for presenting data.
• 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).