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Tier-1 brand, junior level, metro location, and generalist data role increase applicant density.
Data engineering skills are transferable across industries but AWS/Snowflake specificity raises some domain sensitivity.
Multiple mandatory tech skills (AWS, PySpark, Snowflake, Kubernetes, CI/CD) create rigid technical filters.
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Participate in development and implementation of application systems, focusing on AWS-based data engineering solutions.
Develop and debug data processing jobs using Python and PySpark, and work with containerization technologies like Docker and Kubernetes (EKS).
Utilize CI/CD tools (preferably Harness) to automate deployment pipelines and ensure system enhancements align with business needs.
0-2 years of relevant programming and application development experience.
Proficiency with core AWS services including EKS, S3, MWAA, IAM, and VPC.
Programming skills in Python and experience with PySpark for big data processing.
Knowledge of SQL and understanding of containerization tools like Docker and Kubernetes.
Familiarity with CI/CD pipelines, preferably with experience using Harness to streamline deployments.
Basic knowledge of data warehousing concepts, with Snowflake experience considered a plus.
Comfortable working in a technology-focused application development environment supporting data engineering tasks.