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Metro location and demand for data engineers balanced by senior, specialized requirements and non-Tier‑1 employer.
High because required deep ETL, AWS Glue, PySpark, and Kafka expertise limits cross-industry transferability.
High due to explicit 8+ years requirement and multiple mandatory AWS, Glue, PySpark, and Kafka skills.
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Design, develop, and maintain scalable Python-based data pipelines and ETL processes on AWS serving ~4 million users.
Develop and deploy containerized applications and microservices using AWS EKS, Lambda, API Gateway, and integrate with databases like PostgreSQL and AWS DocDB.
Ensure code quality through TDD, collaborate in Agile Scrum environment, and manage infrastructure as code using Terraform.
8+ years of experience designing and developing ETL processes and data pipelines on AWS with Glue, S3, Python, RDS/Aurora, Athena, Event Bridge.
Proficiency in Python and related frameworks with 8+ years of hands-on development experience.
Experience with relational and NoSQL databases including PostgreSQL and AWS DocDB; familiarity with Kafka.
Work Experience Required: 8+ years in relevant ETL and data engineering roles on AWS.
Experienced in high-scale distributed data platforms and ETL pipeline design for millions of users.
Comfortable with Agile Scrum development process and collaborative team environments.
Skilled in leveraging AI-assisted development tools like GitHub Copilot and proficient in CI/CD and infrastructure automation with Terraform.