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Mid-level data engineer with common AWS/Spark/Python skills attracts many qualified applicants, increasing competition.
Core data engineering skills (AWS, Spark, Python, SQL) are highly transferable across industries.
Explicit 4–6 years plus mandatory AWS, Spark, Python, SQL and data modelling creates strict shortlisting filters.
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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 using Apache Spark and Hadoop within AWS ecosystem.
Manage data infrastructure leveraging AWS cloud-native services (S3, EMR, Glue, Redshift, Lambda, Athena) and ensure data quality, security, and governance adherence.
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 strong coding skills in Python and advanced SQL.
Work Experience Required: 4 to 6 years in relevant roles.
Experience operating in cloud-native big data environments on AWS, including serverless and data warehousing architectures.
Strong technical expertise with Spark, Hadoop, ETL/ELT pipeline development, and data modeling for large-scale analytics.
Comfortable implementing data governance, monitoring, and security in distributed data systems.