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Multiple amplifiers: known global brand, metro location, mid-level generalist title, and broad cloud-data skillset.
Data engineering skills (Python, SQL, Airflow, cloud) are highly transferable across industries.
Explicit 3-4 years plus mandatory Python, SQL, Airflow and multi-cloud skills required.
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Design, develop, and maintain scalable, secure data pipelines and ETL/ELT processes using Python, SQL, Apache Airflow, and cloud-native technologies across AWS and GCP.
Support cross-functional teams (EDM, APM, Data Science, Analytics) by delivering reliable data engineering solutions that enhance data availability, accuracy, and quality.
Contribute to cloud modernization efforts and reduce operational overhead via automation, monitoring, and optimization of data workflows.
3-4 years of hands-on data engineering experience building enterprise-scale data pipelines and integration solutions.
Strong proficiency in Python and advanced SQL skills required for data pipeline development and querying.
Experience with Apache Airflow for workflow orchestration and multi-cloud experience with AWS and GCP cloud services.
Bachelor's degree in Computer Science, Computer Engineering, Information Systems, or related technical field, or equivalent practical experience.
Experienced working in multi-cloud environments (AWS and GCP) with strong knowledge of cloud-native data services and architecture patterns.
Proficient in workflow orchestration using Apache Airflow and familiar with Agile development methodologies and source control systems (Git).
Skilled in designing scalable ETL/ELT pipelines and supporting data science, analytics, and business intelligence use cases with attention to data quality and governance.