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Tier-1 employer, popular Data Engineer title, metro location, and broad AWS/PySpark skillset increase competition.
Core data engineering skills (Python, SQL, PySpark, AWS) are highly transferable across industries.
Explicit 0–2 years requirement plus mandatory AWS, PySpark, Python and SQL skills enforce moderate filtering.
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Contribute to design, development, and debugging of application systems, focusing on AWS-related data engineering solutions.
Develop and maintain data processing jobs using Python and PySpark, including deployment with containerization tools (Docker, Kubernetes EKS).
Implement and manage CI/CD pipelines leveraging tools like Harness, integrating with AWS services including EKS, S3, MWAA, IAM, and VPC.
Work Experience Required: 0-2 years in programming/debugging business applications with relevant AWS and data engineering exposure.
Bachelor’s degree or equivalent experience.
Strong proficiency in AWS core services: EKS, S3, MWAA, IAM, VPC.
Technical skills: Python programming, PySpark for big data processing, SQL skills, containerization with Docker and Kubernetes (EKS), experience with CI/CD (preferably Harness).
Intermediate level developer familiar with cloud-native data engineering and container orchestration in AWS environments.
Experience or willingness to implement CI/CD pipelines in an automated software delivery context.
Understanding of data warehousing concepts, with additional familiarity with Snowflake data modeling and tuning as a plus.