Data Engineering - ETL-Python-Graph DB

Location: Hyderabad, India

Experience: 6+ years

Employment Type: Full-time

We are looking for a passionate and experienced Senior Data Engineer with expertise in ETL pipelines, Python programming, and Graph Database technologies. You will be responsible for building scalable data pipelines, designing and implementing data models, and managing data flow across distributed systems to enable business intelligence and advanced analytics.

• Design, develop, and maintain ETL pipelines to ingest, transform, and load large-scale data from various sources.
• Develop data integration solutions using Python and related libraries (Pandas, PySpark, etc.).
• Model, ingest, and query data in Graph Databases such as Neo4j, Amazon Neptune, or ArangoDB.
• Collaborate with Data Scientists, Analysts, and Product teams to deliver high-quality data solutions.
• Optimize data workflows for performance, reliability, and scalability.
• Build data validation and quality frameworks to ensure integrity across pipelines.
• Monitor and troubleshoot data pipelines in production environments.
• Participate in architectural decisions and contribute to best practices in data engineering.

• 6+ years of experience in Data Engineering roles.
• Strong programming skills in Python for data manipulation and automation.
• Proficiency in building and maintaining ETL/ELT workflows using tools like Airflow, Luigi, or custom Python-based frameworks.
• Experience with Graph Databases such as Neo4j, TigerGraph, or Amazon Neptune – including Cypher or Gremlin query languages.
• Experience with SQL and NoSQL databases (PostgreSQL, MongoDB, etc.).
• Solid understanding of data modeling concepts, especially graph data models.
• Experience with cloud platforms (AWS/GCP/Azure) and data storage solutions (S3, Redshift, BigQuery, etc.).
• Strong problem-solving and communication skills.
• Ability to work independently in a fast-paced, agile environment.

• Experience with PySpark or other big data technologies.
• Familiarity with CI/CD pipelines and DevOps practices in data engineering.
• Exposure to data cataloging and metadata management tools.
• Knowledge of data governance and security best practices.