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Software Release Engineer

3 months ago

Lexington, United States Integration International Inc Full time

Responsibilities:

  1. Develop CI/CD Pipelines:
  • Create and operationalize CI/CD pipelines that automate the processes of testing, integration, and deployment for data products.
  1. Containerization:
  • Utilize containerization technologies such as Docker and Kubernetes to encapsulate data processes, ensuring consistent and reliable build and release cycles.
  1. Automation:
  • Automate the validation of components against carefully constructed test datasets to ensure the accuracy and reliability of data products.
  1. Standardization:
  • Develop a consistent and efficient development framework that can be standardized across various data products within the organization.
  1. Collaboration:
  • Work closely with cross-functional teams to ensure the seamless integration of data processes within containers, facilitating smooth build and release operations.
  1. Scalability and Maintainability:
  • Engineer CI/CD workflows that are both scalable and maintainable, enabling rapid iteration and robust deployment patterns.
  1. Software Engineering Best Practices:
  • Apply best practices from software engineering to the data domain to enhance the overall quality and reliability of data products.

Requirements:

  1. DevOps Methodologies:
  • A strong understanding of DevOps practices is essential for this role, including principles of continuous integration and continuous deployment.
  1. Containerization Technologies:
  • Proficiency with containerization technologies such as Docker and Kubernetes is required to encapsulate and manage data processes.
  1. CI/CD Pipeline Engineering:
  • Experience in engineering CI/CD workflows and pipelines, particularly in a data product context.
  1. Test Dataset Validation:
  • Ability to construct and use test datasets to validate data product components throughout the pipeline.
  1. Cross-functional Collaboration:
  • Effective communication and collaboration skills to work with various teams involved in the data product lifecycle.
  1. Software Engineering Best Practices:
  • Knowledge of best practices in software engineering specifically applied to data product development and deployment.