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Strong Tier-1 brand, mid-level role, metro context and generalist AWS/Spark skills increase competition.
AWS Spark ETL skills are broadly transferable, banking compliance adds moderate domain specificity.
Explicit 5+ years and many mandatory AWS/Spark/ETL/infra skills create strict filtering.
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Designs, develops, and troubleshoots end-to-end ETL/ELT data pipelines on AWS, ensuring secure, scalable, and high-quality production code.
Optimizes Spark jobs and monitors performance using CloudWatch and Spark UI to improve operational stability and efficiency.
Leads technical evaluations of external vendors and drives adoption of new technologies across software engineering teams.
5+ years of applied experience in software engineering with formal training or certification.
Experience designing and implementing ETL/ELT pipelines on AWS using AWS Glue (PySpark) and/or EMR/EMR Serverless.
Proficient with AWS services including S3, Glue Data Catalog, CloudWatch, IAM, KMS, Secrets Manager, Lake Formation, and workflow orchestration tools like AWS Step Functions or Airflow (MWAA).
Work Experience Required: 5+ years
Experienced in building and optimizing data lakes/lakehouses using columnar formats (Parquet/ORC) with best practices in partitioning and schema evolution.
Skilled in data modeling methodologies and driving CI/CD for data engineering with infrastructure as code (Terraform or AWS CDK).
Capable of leading architectural reviews and driving operational excellence, including security compliance and automation of recurring incidents.