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Mid-level AWS data engineer in metro with common skills driving high qualified applicant density.
Requires AWS data stack and Spark expertise, making skills moderately transferable across industries.
Explicit 5–8 year requirement plus mandatory AWS, Spark, Redshift, and IaC skills makes filters strict.
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Architect and implement scalable, secure, and cost-effective data platforms on AWS with strong focus on performance and cost efficiency.
Lead the design and development of complex ETL/ELT pipelines and data workflows ensuring data quality, governance, and security.
Define and enforce data engineering standards, conduct code reviews, and collaborate with cross-functional teams to deliver high-quality data solutions.
Bachelor’s degree in computer science, engineering, or related field.
5-8 years of total data engineering experience, with at least 3 years of hands-on expertise in AWS Cloud big data platforms.
Proficiency in Python, SQL, Apache Spark, and AWS data services including Glue, S3, Redshift, Lambda, CloudWatch, IAM.
Experience with DevOps practices and tools such as CI/CD, infrastructure-as-code (Terraform or CloudFormation), and Git.
Experienced in architecting and optimizing AWS-based data platforms with a focus on scalability, security, and cost-effectiveness.
Strong technical leadership demonstrated by ownership of data engineering standards, code reviews, and collaboration with multiple stakeholder teams.
Practically skilled in building and maintaining complex ETL/ELT pipelines and data workflows using AWS tools and big data technologies.