Position: Senior Data Ops Engineer
Company: Zoolatech
Category: DevOps
Experience required: 5y
English level: upper
Published: 2023-07-01T00:00:00+03:00

Responsibilities:

• Deploy and manage services and infrastructure to support our clients' data at scale.
• Transform capabilities built by Data Engineers into real-world data production systems.
• Develop and deploy scalable tools and services for our clients to manage their data within MasterControl.
• Identify and evaluate novel technologies to enhance performance, maintainability, and reliability of our data systems.
• Implement software engineering best practices to data management, including CI/CD, automation, etc.
• Support all our data activities focusing on auditability, versioning, and data security.
• Enable the development and deployment of proof-of-concept data systems.

Skills Required:

• AWS, Kubernetes, Terraform.
• Experience maintaining and managing data systems.
• Robust software engineering skills in intricate, multi-language systems.
• Comfort with Linux administration.
• Experience working with cloud computing and database systems.
• Experience managing and operating custom integrations between cloud-based systems using APIs.
• Experience maintaining and operating data systems built with open-source tools.
• Experience developing with containers and Kubernetes in cloud computing environments.
• Strong understanding of mechanisms for robust observability.
• Knowledge of data processing methodology and best practices.
• Familiarity with data modeling frameworks.

Project Description.

As part of our team, you'll be deploying cutting-edge data management solutions for MC clients. Imagine a scenario where MC Data Engineers devise a ground-breaking solution for automatically reading, processing, and categorizing thousands of documents. The solution performs flawlessly in a development environment, but how should it be transitioned into production? How will end users interface with the solution? How will it scale to processing millions of documents? What tools or platforms should we and our clients use for monitoring? A DataOps engineer at MC must address these questions AND implement the solution.