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Senior, specialized skillset but metro location and recognized employer create moderate competition.
Core data engineering skills (ETL, Spark, Kafka) are broadly transferable across industries.
Multiple mandatory technical skills (PySpark, Kafka, Airflow, advanced SQL) increase filter strictness despite no years specified.
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Own the design, development, and maintenance of robust ETL pipelines using Python, PySpark, and SQL.
Implement and optimize big data architectures and streaming data processing using Apache Kafka and Apache Spark.
Ensure data quality, validation, and integration within complex, mission-critical applications.
Advanced proficiency in Python and production-level experience with PySpark.
Expert-level SQL skills including complex query writing.
Solid understanding of big data concepts and experience with Apache Kafka, Apache Airflow, and stream processing.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in building scalable data engineering solutions in big data environments.
Skilled in stream processing and data quality validation to support data-driven applications.
Familiarity with cloud data warehousing (Snowflake) and query optimization is a plus but not mandatory.