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Mid-level, metro location, popular cloud-data title, and broad toolset increase applicant competition.
Core cloud data engineering skills are transferable across industries, though finance/healthcare domain preference raises sensitivity slightly.
Mandatory 6+ years and specific Snowflake/DBT/Airflow/AWS skills create strict shortlisting filters.
Design, develop, and optimize scalable, cloud-based data pipelines primarily using AWS, Snowflake, Airflow, and DBT for enterprise analytics and data warehousing.
Automate data workflows, deployment pipelines, and infrastructure provisioning with CI/CD and Infrastructure as Code tools such as Terraform and CloudFormation.
Monitor, troubleshoot, and optimize large-scale cloud data architectures, supporting migration and cloud adoption projects aligned with enterprise objectives.
5+ years of experience supporting enterprise cloud data pipelines, migration, and automation in large organizations.
Proven hands-on expertise with AWS services (S3, Lambda, Glue, EC2), Snowflake, Apache Airflow, DBT, and SQL.
Bachelor’s or Master's degree in Computer Science, Data Science, or related fields.
Work Experience Required: At least 5 years in enterprise cloud data environments with automation and pipeline management.
Experienced in designing and managing high-throughput, scalable data pipelines in complex, large-scale environments with cross-functional teams.
Strong technical skills in AWS cloud technologies combined with Snowflake data warehousing and orchestration via Airflow and DBT for modular data transformation.
Capability to handle end-to-end cloud data migration, infrastructure as code automation, and performance tuning aligned with enterprise analytics and compliance standards.