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Mid-level Data Engineer in metro at a known employer creates moderate competition from generalist applicants.
Core data engineering skills (Spark, AWS, Python, SQL) are highly transferable across industries.
Explicit 4–6 years requirement plus mandatory Spark, AWS, Python and SQL increases filtering.
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Design, build, test, and maintain scalable ETL/ELT data pipelines using Python, Spark, and Pyspark.
Architect and optimize distributed big data processing systems within AWS cloud services, including S3, EMR, Glue, Redshift, Lambda, and Athena.
Implement data warehousing solutions with dimensional modeling and optimize query performance; enforce data quality, monitoring, and security standards.
4 to 6 years of professional experience in data engineering or software development.
Hands-on experience building data lakes and warehouses on AWS.
Proficiency in Apache Spark and at least one streaming data technology (Kafka or Kinesis).
Strong Python programming and advanced SQL skills; solid understanding of dimensional data modeling.
Experienced in architecting and operating large-scale, cloud-native big data infrastructures leveraging AWS ecosystem.
Skilled in distributed computing frameworks and building robust, automated data pipelines.
Familiar with data governance, security best practices, and performance optimization in analytics environments.