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Tier-1 brand, mid-level generalist data-engineer, and metro location increase applicant competition.
Core AWS data engineering skills are broadly transferable, though banking compliance adds moderate domain bias.
Multiple mandatory AWS data-engineering tools plus explicit 5+ years increases filter strictness.
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Design, develop, and maintain end-to-end ETL/ELT data pipelines on AWS using Glue, EMR, and related services for data lakes and lakehouses.
Optimize Spark jobs and enforce data quality, observability, security, and compliance measures including IAM, encryption, and access controls.
Lead technical evaluation of vendors and solutions; drive communities of practice to adopt new technologies; contribute to architectural reviews and CI/CD pipeline improvements.
5+ years of applied software engineering experience with formal training or certification.
Expertise in building ETL/ELT pipelines on AWS Glue (PySpark) and EMR/EMR Serverless.
Strong knowledge of data lake/lakehouse architectures using Amazon S3 and columnar formats (Parquet/ORC).
Proficiency in security and compliance best practices on AWS including IAM, KMS, VPC endpoints, Secrets Manager, and Lake Formation.
Experienced in large-scale cloud data engineering within AWS ecosystem focusing on scalable and secure data architectures.
Able to lead cross-team technical evaluations and foster adoption of advanced technologies in software engineering.
Comfortable driving automation and operational improvements through CI/CD, infrastructure as code, and workflow orchestration tools like Step Functions or Airflow.