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Strong employer brand, metro location, and broad senior tech requirements drive high candidate competition.
Data engineering skills are widely transferable, though financial-services consulting raises moderate domain specificity.
Explicit 8–12 years plus mandatory AWS, Databricks, DBT, and IaC skills create high shortlisting strictness.
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Develop and maintain scalable data pipelines and ETL processes using Python, PySpark, Spark SQL, and DBT for data transformation and modeling.
Lead data migration from legacy platforms to AWS with design and implementation of modern cloud-based data architectures.
Implement and optimize data engineering workflows on Databricks and AWS services including S3, Glue, Redshift, Athena, EMR, Lambda, ensuring CI/CD and Infrastructure as Code practices.
8-12 years of experience as an AWS Data Engineer or in a similar role.
Bachelor's degree in Computer Science, Engineering, or related field, or equivalent experience.
Hands-on expertise with Python, PySpark, Spark SQL, DBT, Databricks, and AWS data services (S3, Glue, Redshift, Athena, EMR, Lambda, IAM).
Experience with CI/CD, Infrastructure as Code (Terraform or CloudFormation), Git, and Agile methodologies.
Demonstrates strong technical leadership capabilities to guide and mentor team members.
Experienced in designing and executing cloud-native data architectures and managing migrations to AWS environments.
Practiced in automation and software engineering best practices within data engineering workflows, particularly leveraging Databricks and AWS services.