Design, build, and maintain scalable ETL pipelines for diverse data sources
Develop and optimize data processing workflows and models for performance and reliability
Leverage Apache Spark for distributed data transformations and large-scale processing
Schedule and manage data pipelines using Apache Airflow
Write efficient, maintainable Python code for data tasks and automation
Model and manage graph databases using Neo4j to extract insights from complex data relationships
Collaborate with data scientists, analysts, and cross-functional teams to deliver actionable insights
Maintain high data quality through testing, validation, and monitoring
Troubleshoot pipeline issues and ensure infrastructure reliability
Stay current with trends and advancements in data engineering

Qualifications:

Proven experience as a Data Engineer or similar role
Proficiency in Python, including libraries like Pandas and PySpark
Strong understanding of ETL processes and tools
Hands-on experience with Apache Spark and Airflow
Practical knowledge of Neo4j and Cypher for graph-based data modeling
Solid understanding of SQL and NoSQL databases
Familiarity with cloud platforms like AWS, GCP, or Azure
Strong analytical thinking and problem-solving skills
Excellent collaboration and communication abilities
Bachelor’s degree in Computer Science, Engineering, or a related field

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