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Popular data-engineer role with common Spark/Airflow skills attracts moderate competition.
Core data engineering skills are highly transferable across industries despite Neo4j niche requirement.
Multiple mandatory tech requirements (Spark, Airflow, Neo4j, Python) increase screening strictness.
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Design, build, and maintain scalable ETL pipelines to ingest and process data from various sources.
Develop and optimize data workflows using Apache Spark and orchestrate pipelines through Apache Airflow ensuring timely delivery.
Model and manage graph databases using Neo4j to extract insights and support data-driven initiatives.
Proven experience as a Data Engineer or in a similar role.
Strong proficiency in Python including data libraries like Pandas and PySpark.
Hands-on experience with Apache Spark, Apache Airflow, ETL processes, and Neo4j graph databases.
Bachelor's degree in Computer Science, Engineering, or related field.
Operates with a strong focus on building scalable data infrastructure and workflow automation.
Experienced in managing complex distributed data processing environments with Apache Spark and Airflow.
Possesses a strong background in graph database modeling with Neo4j and practical application of Cypher queries.