Degree in Computing Science, Data Engineering, Information Technology, or related discipline.
3+ years of experience in a Data Engineering role.
Experience with event-driven architecture and related technologies (e.g., Apache Kafka, Amazon MSK).
Familiarity with Infrastructure as Code (IaC) and CI/CD best practices in multi-environment deployment workflows, including hands-on experience with Terraform and GitHub Actions.
Experience with monitoring and alerting platforms such as CloudWatch, Datadog, or PagerDuty.
Hands-on experience with ETL/ELT and orchestration tools including AWS Glue Studio, Matillion, and AWS Step Functions.
Proficiency with AWS data services: Redshift, EMR, S3, Lambda, Kinesis, and MSK.
Strong proficiency in SQL, Python, Java, and/or Scala.
You're a pragmatic builder and problem-solver who's passionate about enabling better decisions through data.
You understand that clean architecture and thorough documentation are essential, not optional.
You work well in agile teams and enjoy collaborating with product, engineering, and analytics partners.
You have opinions on data modeling, enjoy the challenge of working with event-driven architectures, and are comfortable switching between SQL, Python, Java, Scala and a variety of AWS services.
You have embraced AI-assisted development as a core part of your workflow — leveraging agentic tools such as Claude Code, Amazon Q Developer, and GitHub Copilot to accelerate delivery, reduce toil, and maintain higher standards of code quality.
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