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Mid-level popular data-engineer role, metro location and 4–6 years amplify competition.
Data engineering skills (AWS, ETL, SQL) are broadly transferable across industries.
Explicit 4–6 years plus mandatory AWS Glue/Redshift/Python skills increase screening rigidity.
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Design, develop, and maintain scalable data ingestion, transformation pipelines, and data warehouses within the AWS ecosystem.
Build and optimize ETL/ELT workflows using AWS Glue, develop automation and orchestration solutions in Python, and support relational and NoSQL databases including Amazon Redshift, Snowflake, Amazon RDS, and DynamoDB.
Ensure data quality, security, and cost-efficiency while collaborating cross-functionally to deliver business-focused cloud data solutions.
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 and/or Snowflake, Amazon RDS, Amazon DynamoDB, and AWS Lambda.
Proficiency in Python and strong SQL skills for data processing, automation, and data modeling.
Experienced in designing and managing large-scale cloud-based data pipelines and warehouse architecture on AWS.
Skilled in building serverless, event-driven data processing systems and integrating multiple AWS services effectively.
Comfortable working in agile environments and collaborating with cross-functional teams to translate business requirements into technical data solutions.