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Mid-level Data Engineer in a metro with common skills and experience attracts strong candidate competition.
Technical AWS and PySpark skills are transferable across industries but require data-specific experience.
Explicit 4–7 years and mandatory PySpark, Python, AWS, and IAM requirements enforce strict filtering.
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Design, build, and support end-to-end data pipelines on AWS infrastructure.
Develop and optimize distributed data processing workflows using Python and PySpark.
Manage and secure AWS resources including IAM roles, policies, and monitoring tools for data services.
4–7 years of hands-on experience as a Data Engineer working with AWS data services.
Proficiency in Python, PySpark, and strong SQL skills for complex query writing and optimization.
Hands-on experience with AWS core and data analytics services: Lambda, RDS, EMR, Glue, Lake Formation, DynamoDB, IAM.
Experience in IAM role design, policy management, and enforcing least-privilege access.
Experienced in building scalable, distributed data pipelines with strong operational reliability focus.
Well-versed in AWS ecosystem specifically for data engineering and security best practices.
Capable of deep troubleshooting and root cause analysis across complex data workflows and AWS monitoring systems.