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Mid-level AWS data engineer in a metro location with broad tooling demands increases applicant competition.
AWS and Spark data engineering skills are transferable across industries but require cloud-specific expertise.
Explicit 6+ years requirement plus mandatory AWS, Spark, IaC and Airflow makes filters strict.
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Design and implement scalable, secure, and highly available data architectures on AWS for data lakes, warehouses, and streaming platforms.
Build, optimize, and maintain ETL/ELT data pipelines processing massive datasets, ensuring cost-efficient use of AWS resources.
Lead data architecture technical decisions, enforce data governance and security, and mentor junior engineers while collaborating with data scientists and product owners.
6+ years of experience in Data Engineering, with at least 2 years in data architecture and system design.
Proficient with AWS data services including compute, storage, and streaming components.
Strong skills in Python or Scala, Apache Spark, master-level SQL, and data warehousing concepts.
Experience with Infrastructure as Code (Terraform, CloudFormation, or AWS CDK), Apache Airflow, and Git-based CI/CD pipelines.
Senior-level engineer capable of bridging data architecture and implementation in AWS cloud environments.
Experienced in architecting cost-efficient, secure data ecosystems focusing on analytics, reporting, and AI/ML use cases.
Able to lead technical authority for data solutions and mentor junior staff while collaborating cross-functionally with data science and product teams.