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Mid-level data-engineer role with broad AWS stack, metro office, and known employer increases competition.
Core cloud data engineering skills (AWS, Glue, Redshift, Snowflake) are highly transferable across industries.
Explicit 4–6 years requirement plus mandatory AWS Glue, Redshift/Snowflake, and Python skills make shortlisting strict.
Design, develop, and maintain scalable AWS data ingestion, transformation, and processing pipelines using services like AWS Glue, Lambda, Redshift, Snowflake, RDS, and DynamoDB.
Ensure data quality, security, governance, and optimize cloud data architectures for performance and cost efficiency.
Collaborate with cross-functional global teams to deliver reliable data solutions supporting strategic business decisions.
Bachelor’s degree in Computer Science, Information Systems, Engineering, or related technical field.
4–6 years of experience in Data Engineering, Data Integration, or Cloud Data Platform development.
Hands-on experience with AWS data and analytics services including AWS Glue, Amazon Redshift, Snowflake, Amazon RDS, Amazon DynamoDB, and AWS Lambda.
Proficiency in Python and strong SQL skills for data processing, automation, and workflow development.
Experienced in designing and supporting large-scale, cloud-native data pipelines and data warehouse solutions on AWS.
Demonstrates ability to work cross-functionally in agile environments, engaging both technical and non-technical stakeholders.
Holds or is pursuing relevant AWS certifications and is familiar with infrastructure as code, CI/CD pipelines, data governance, and cloud platform best practices.