Key Responsibilities:Data Pipeline Development: Design, develop, and maintain scalable data pipelines using SPARQL and Python to efficiently extract, transform, and load (ETL) data into graph databases.Query Optimization: Create and optimize complex SPARQL queries to retrieve and analyze data from graph databases, ensuring performance and accuracy.Graph-Based Applications: Develop graph-based applications and models to address real-world challenges and derive valuable insights from data.Collaboration: Work closely with data scientists and analysts to understand their data needs and translate them into effective data pipelines and models.Data Quality Assurance: Ensure data quality and integrity throughout the data pipeline process, implementing checks and balances as necessary.Continuous Learning: Stay updated with the latest advancements in graph databases, data modeling, and relevant programming languages to enhance skills and contribute effectively.Qualifications:Education: Bachelor’s degree in Computer Science, Data Science, or a related field.Technical Skills: Proven experience with SPARQL and Python programming.Graph Database Knowledge: Strong understanding of graph databases (e.g., RDF, Neo4j, GraphDB).Data Modeling Experience: Familiarity with data modeling and schema design principles.Data Pipeline Tools: Knowledge of data pipeline tools and frameworks (e.g., Apache Airflow, Luigi).Problem-Solving Skills: Excellent analytical and problem-solving abilities.Teamwork: Ability to work independently and collaboratively as part of a team.