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Known employer, metro location, mid-level generalist data role with broad cloud and ETL requirements.
Core data engineering skills are highly transferable across industries; healthcare experience is only advantageous.
Explicit 2–5 years plus mandatory cloud, Spark/Databricks, SQL and Python skills.
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Develop and maintain scalable, secure cloud-based data platforms and pipelines to enable business insights, analytics, automation, and AI capabilities.
Collaborate with product owners, engineering teams, and business stakeholders to deliver high-quality data engineering solutions meeting quality and timeline targets.
Support troubleshooting, monitoring, deployment, and continuous improvement of data engineering initiatives aligned with business goals.
2-5 years of experience in software engineering or data engineering with cloud-native data solutions experience.
Proficient with AWS cloud services (e.g., S3, Lambda, IAM, API Gateway) and data engineering tools such as AWS Glue, Spark, or Databricks.
Bachelor's degree in Computer Science, Data Engineering, Software Engineering, Information Technology, or a related field.
Work Experience Required: 2-5 years (explicitly mentioned).
Experienced working with cloud infrastructure as code tools like Terraform or CloudFormation and CI/CD pipelines using GitHub Actions, Jenkins, or GitLab CI.
Comfortable collaborating with both technical and non-technical stakeholders in a team environment, contributing to project delivery and process improvements.
Exposure to AI technologies including GenAI platforms (e.g., AWS Bedrock) and familiarity with AI productivity tools and prompt engineering techniques enhances candidacy.