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Mid-level, popular data engineering role with broad skillset requirements increases applicant competition.
Core data engineering skills are broadly transferable across industries.
Explicit 5+ years and many mandatory cloud, SQL, and pipeline technologies increase filtering.
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Build and expand data pipelines and data platform using latest technologies including Google Cloud infrastructure.
Develop and deploy applications leveraging cloud services, Docker, Kubernetes, and Terraform.
Integrate machine learning models into data workflows and manage orchestration using tools like Airflow or Cloud Composer.
5+ years of experience in Big Data and Data Engineering.
Strong proficiency in SQL, Python/PySpark, and data warehousing concepts including BigQuery.
Experience with one or more public cloud platforms (GCP, Azure, AWS) and container orchestration using Docker and Kubernetes.
Bachelor's or advanced degree in Computer Science or related engineering discipline.
Experienced in designing and developing scalable ETL/ELT pipelines optimized for cloud environments.
Familiar with workflow management tools (Airflow, AWS Data Pipeline, Google Cloud Composer) and CI/CD using Azure DevOps or similar tools.
Capable of managing containerized deployments using Kubernetes and infrastructure as code tools like Terraform.