Responsibilities:

• Develop high-quality Python libraries and packages that address specific needs within the user community. Write clean, efficient, and well-documented code adhering to established coding standards and best practices.
• Provide guidance for developing modern APIs ensuring resilience, high availability, scalability and security.
• Support our cloud transformation, including IaC using Terraform and Azure pipelines for CI/CD.
• Implement new features, enhancements, and bug fixes in response to user feedback and community contributions. Ensure compatibility of Python libraries with different versions of Anaconda Distribution and other popular Python distributions.
• Monitor infrastructure health, performance, and resource utilization to ensure optimal operation of Anaconda Enterprise services.
• Configure user authentication mechanisms, access controls, and permissions within the Anaconda Enterprise platform.
• Manage user accounts, groups, and roles to enforce security policies and ensure compliance with organizational guidelines.
• Customize Anaconda Enterprise environments to meet the specific needs of data science teams and projects.
• Install and manage additional packages, libraries, and dependencies required for data analysis, machine learning, and other tasks.
• Provide technical support and troubleshooting assistance to users encountering issues with Anaconda Enterprise platform usage.
• Diagnose and resolve infrastructure, configuration, and compatibility issues affecting Anaconda Enterprise deployments.
• Monitor and optimize the performance of Anaconda Enterprise environments, including system responsiveness, resource utilization, and job execution times.
• Identify and address bottlenecks, inefficiencies, and scalability challenges to ensure smooth operation of data science workflows.

Requirements:

• Strong proficiency in Python programming language.
• Solid experience with Python frameworks such as FastAPI, Flask, Django.
• Experience with Python Package Managers, especially Conda.
• Experience with cloud platforms (e.g. AWS, Azure).
• Experience with DevOps principles, and security scanning tools.
• Understanding of data science concepts and methodologies. Experience with machine learning or MLOps is a nice to have.
• Familiarity with data manipulation and analysis libraries such as pandas, NumPy, and SciPy.
• Understanding of containerization technologies like Docker and Kubernetes.