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Mid-level popular data role in metro cities at a known consulting firm creates high competition density.
Cloud data engineering skills (AWS, PySpark, SQL, NoSQL) are highly transferable across industries, so sensitivity is low.
Explicit 5–8 years plus mandatory AWS, PySpark, CI/CD, and production support skills indicate high strictness.
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Design, develop, and maintain scalable data engineering and ETL solutions using Python, PySpark, and AWS services including Glue, Lambda, Redshift, and Step Functions.
Develop, optimize, and troubleshoot large-scale PySpark applications and AWS-based data pipelines with performance tuning and root-cause analysis responsibilities.
Implement CI/CD pipelines, containerized deployments (Docker, Kubernetes/EKS/ECS), and production monitoring to ensure reliable delivery and support.
5-8 years of hands-on experience in AWS, Python, PySpark, SQL, and NoSQL databases (MongoDB).
Bachelor's degree in Computer Science or related field with at least 4 years of relevant experience.
Experience with AWS services (IAM, Glue, Lambda, Redshift, CloudWatch), container orchestration (Kubernetes, EKS/ECS) and CI/CD tools like Jenkins.
Experience developing and supporting scalable cloud data pipelines, and debugging production issues.
Strong expertise in large-scale data processing with PySpark optimization and debugging in cloud environments.
Proven experience managing full data engineering lifecycle including development, testing, deployment, monitoring, and production support in Agile teams.
Capable of collaborating across technical and business stakeholders, with ownership of complex delivery and production incident resolution.