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Mid-level generalist data engineer in metro with broad AWS/SQL/Python requirements increases competition.
Core AWS, SQL, Python and Spark skills are easily transferable across industries.
Explicit 3–7 years plus mandatory AWS, SQL and Python requirements make filtering strict.
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Develop, build, and maintain scalable, secure, high-performance data platforms on AWS focusing on data pipeline development and cloud data engineering.
Design and implement ETL/ELT pipelines using AWS services including S3, Glue, EMR, Athena, and Redshift.
Monitor pipeline performance and reliability, troubleshoot data issues with root cause analysis, and drive best practices in security, scalability, and cost efficiency.
3 to 7 years of experience in data engineering with hands-on AWS experience.
Strong proficiency in SQL and Python (mandatory), including complex queries and data pipeline scripting.
Bachelor’s or Master’s degree in Computer Science, IT, Data Engineering, or related fields.
Experience with AWS data services such as S3, Glue, Athena, Redshift, EMR and knowledge of batch and streaming data processing.
Individual contributor skilled in designing cloud-native data lakes and data warehouse architectures with emphasis on performance and cost optimization.
Experienced in working with global delivery teams and collaborating with architects, DevOps, QA, and business stakeholders.
Proficient in implementing infrastructure as code (Terraform/CloudFormation) and CI/CD pipelines for data workloads with a focus on production reliability and monitoring using AWS CloudWatch.