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Popular data engineer role with broad PySpark/Databricks/AWS requirements attracts many qualified applicants.
Core data engineering skills (Python, PySpark, Databricks, AWS) are highly transferable across industries.
Explicit 7-10 years plus mandatory PySpark, Databricks and AWS skills create strict technical filters.
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Design, develop, and maintain scalable batch and real-time data pipelines and ingestion frameworks for diverse data types.
Optimize data transformation workflows and cloud resource usage, ensuring data quality, security, and governance in enterprise-grade cloud data platforms.
Collaborate with cross-functional teams to translate business requirements into technical data solutions and contribute to architecture and best practice implementation.
7-10 years of experience in Data Engineering, Big Data, or Cloud Data Platforms.
Strong programming skills in Python, with hands-on expertise in PySpark and Apache Spark.
Extensive experience with Databricks, Delta Lake, Lakehouse architecture, SQL, and AWS Cloud Platform.
Experience with ETL/ELT pipeline development, data modeling, schema design, workflow orchestration tools (e.g. Apache Airflow), and version control using Git.
Experienced in building and optimizing enterprise-scale cloud-native data engineering solutions within AWS environments.
Skilled in both batch and real-time data pipeline development and performance tuning for cost efficiency.
Capable of collaborating effectively with data scientists, analysts, and architects to align technical implementations with business goals.