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Mid-level, metro role with broad AWS/Spark requirements increases applicant density.
Strong AWS data engineering, Spark, and Redshift requirements limit cross-industry transferability.
Explicit 6–10 years plus mandatory AWS, Python, SQL, Spark, and IaC skills impose stringent filters.
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End-to-end technical ownership of designing and delivering scalable, secure, and high-performance data platforms using AWS data services.
Build, optimize, and maintain batch and streaming data pipelines, ensuring production stability, performance tuning, and cost optimization.
Collaborate with global teams, architects, and clients to translate business requirements into enterprise-grade data engineering solutions and participate in solution design and architecture discussions.
6 to 10 years of relevant experience in AWS data engineering roles.
Strong hands-on skills with AWS services including Amazon S3, AWS Glue, Athena, and Redshift.
Mandatory expertise in SQL and Python programming for data transformations and pipeline development.
Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Engineering, or related field.
Technical lead with strong individual contributor experience and ownership mindset in building cloud-native data lakes and data warehouses on AWS.
Experience with distributed processing frameworks like Spark/PySpark and designing fault-tolerant, performant data workflows.
Familiarity with infrastructure as code (Terraform/CloudFormation), CI/CD pipeline development, and working in Agile delivery models with global teams.