Data Engineer (Python + SQL / Sparql + Graph Data)

Key Responsibilities

• Design, develop, and optimize ETL and data pipelines for large-scale data processing
• Write and optimize complex SQL queries including joins, subqueries, CTEs, window functions, and performance tuning
• Perform data transformation, validation, cleansing, and data quality checks
• Build and maintain scalable data ingestion pipelines using APIs, SFTP, Kafka, and related integration tools
• Work with large distributed datasets and ensure efficient data processing
• Design and implement graph data models including nodes, edges, and relationships
• Work with RDF data structures and semantic data frameworks
• Develop and optimize SPARQL queries for graph data retrieval and analysis
• Build and maintain knowledge graphs and ontology-driven data systems
• Automate data workflows and operational tasks using Python scripting
• Monitor, troubleshoot, and optimize pipeline performance and reliability
• Required Skills & Experience
• Strong hands-on experience with Python, including Pandas, NumPy, and automation scripting
• Advanced SQL expertise including query optimization, CTEs, subqueries, joins, and window functions
• Strong understanding of ETL concepts, data engineering practices, and pipeline architecture
• Experience working with large-scale distributed data processing environments
• Solid understanding of graph data concepts including nodes, edges, and relationships
• Experience with RDF data models and SPARQL querying
• Experience with data ingestion and integration tools such as APIs, Kafka, and SFTPStrong understanding of data transformation, cleansing, and performance optimization techniques
• Nice to Have
• Experience with graph databases such as Neo4j or Blazegraph
• Exposure to cloud platforms including AWS, Azure, or GCPExperience with workflow orchestration tools such as Airflow
• Understanding of data lake and data warehousing concepts
• Preferred Profile
• Strong analytical and problem-solving skills
• Ability to work in complex data-driven environments
• Experience collaborating with cross-functional engineering and data teams
• Self-driven mindset with strong attention to detail and performance optimization