Data Engineer at GFT managing data systems and workflows, focusing on data engineering and data science collaboration. Leveraging technologies like Python, Airflow, and AWS.
Responsibilities
Architect, develop, and maintain scalable data infrastructure, including data lakes, pipelines, and metadata repositories
Work closely with data scientists to build and support data models, integrate data sources, and support machine learning workflows and experimentation environments
Develop and optimize large-scale, batch, and real-time data processing systems to enhance operational efficiency and meet business objectives
Leverage Python, Apache Airflow, and AWS services to automate data workflows and processes
Utilize AWS services such as S3, Glue, EC2, and Lambda to manage data storage and compute resources
Implement robust testing and validation procedures to ensure the reliability, accuracy, and security of data processing workflows
Stay informed of industry best practices and emerging technologies in both data engineering and data science
Requirements
Proficiency in Python for data processing and scripting (pandas, pyspark)
Workflow automation (Apache Airflow)
Experience with AWS services (Glue, S3, EC2, Lambda)
Experience with Kubernetes and Docker for managing containerized environments in the cloud
Hands-on experience with columnar and big data databases (Athena, Redshift, Vertica, Hive/Hadoop)
Strong familiarity with AWS services for cloud-based data processing and management
Experience with CI/CD tools such as Jenkins, CircleCI, or AWS CodePipeline for continuous integration and deployment
Expertise in building and managing robust data architectures and pipelines for large-scale data operations
Ability to support data science workflows, including collaboration on data preparation, feature engineering, and enabling experimentation environments
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