Azure DataOps - DevOps Engineer

About Fusemachines

Fusemachines is a leading AI strategy, talent, and education services provider. Founded by Sameer Maskey Ph.D., Adjunct Associate Professor at Columbia University, Fusemachines has a core mission of democratizing AI. With a presence in 4 countries (Nepal, the United States, Canada, and the Dominican Republic and more than 350 full-time employees) Fusemachines seeks to bring its global expertise in AI to transform companies around the world.

Qualification & Experience

• Must have a full-time Bachelor's degree in Computer Science or similar
• At least 3 years of working experience in DevOps engineering
• 3+ years of experience with Azure DevOps, Azure Cloud Platform, and other hyperscalers.
• Proven experience delivering projects for Data and Analytics, including data engineering, data analytics and advanced analytics tools and technologies
• Following certifications:
  • Microsoft Certified: Azure Fundamentals
  • Microsoft Certified: Azure DevOps Engineer Expert
  • Microsoft Certified: Azure Data Engineer Associate
  • Microsoft Certified: Azure Solutions Architect Expert

Required skills/ Competencies

• Strong understanding of the software development lifecycle (SDLC), including Agile methodologies
• Strong understanding of DevOps principles, including continuous integration, continuous delivery (CI/CD), infrastructure as code (IaC), configuration management, automated testing and cost management
• Strong knowledge in at least one of the programming/scripting languages, like Python, Bash and PowerShell to automate tasks, create deployment scripts, and manage infrastructure
• Hands-on experience using tools like Azure Resource Manager templates, Terraform, or Ansible to define and manage infrastructure.
• Deep knowledge of Microsoft Azure services related to data and analytics, such as Azure Data Factory, Azure Databricks, Azure Synapse Analytics (formerly SQL Data Warehouse), Azure Stream Analytics, SQL Server, PowerBI, Azure DevOps, Azure Blob Storage, Azure Data Lake Storage, Azure SQL Database, etc.
• Knowledge of data pipeline design and ELT/ETL processes to efficiently move, transform, and process data within Azure environments.
• Robust understanding in deploying containers using Kubernetes, working with multiple containers and services. Should be able to scale, secure and configure Kubernetes clusters
• Ability to set up monitoring and alerting using Azure Monitor, Application Insights, or other monitoring tools like Datadog, CloudWatch, Prometheus, SumoLogic or NewRelic..
• Skill in aggregating and analyzing logs to identify performance bottlenecks and troubleshoot issues.
• Understanding of Azure security practices, including network security groups, Azure Active Directory, encryption, and compliance standards.
• Ability to implement security controls and best practices within data and analytics solutions, including proficient knowledge and working experience on various cloud security vulnerabilities and ways to mitigate them. Should be able to research thoroughly and implement industry standard tools and necessary patches to minimize & mitigate security loopholes as and when required.
• Hands-on experience with SQL and NoSQL databases. SQL Server is a must
• Good grasp on UML Diagram such as Use Case Diagram, Flow Diagram
• Effective communication skills to collaborate with cross-functional teams, including data engineers, data analysts, data scientists, developers, and operations teams
• Ability to document processes, procedures, and deployment configurations
• Strong analytical skills to identify and address technical issues, performance bottlenecks, and system failures
• Proficiency in debugging and troubleshooting issues in complex data and analytics environments
• A willingness to stay updated with the latest Azure services, DevOps trends, and best practices in the field

Responsibilities

• Design, implement, and maintain the cloud infrastructure required for data and analytics solutions in Azure.
• Configure and manage virtual machines, networks, storage, and other resources as needed.
• Establish secure access controls, networking configurations, and compliance measures.
• Set up and maintain CI/CD pipelines for data pipelines, analytics workflows, and applications.
• Automate build, test, and deployment processes to ensure a consistent and efficient release cycle.
• Monitor and enhance pipeline performance, reliability, and scalability.
• Manage code repositories using version control systems (e.g., Git) and collaborate with development teams to ensure code quality and version management.
• Implement branching strategies, code reviews, and merge processes to maintain a structured development environment.
• Create and manage infrastructure as code templates using tools like ARM templates or Terraform to enable consistent and repeatable provisioning.
• Update and maintain infrastructure configurations as application requirements evolve.
• Design, build, and maintain data pipelines for ELT/ETL processes using Azure Data Factory, Databricks, or other appropriate tools.
• Monitor pipeline performance, troubleshoot issues, and optimize data flow and transformation processes.
• Implement monitoring solutions using Azure Monitor, Application Insights, or similar tools to ensure application and system health.
• Analyze performance metrics, identify bottlenecks, and optimize applications and services for scalability and efficiency.
• Implement security measures such as identity and access management, encryption, and network security groups to protect data and applications.
• Ensure compliance with relevant data protection regulations and industry standards.
• Containerize applications using Docker and manage them within a Kubernetes cluster if applicable.
• Set up Kubernetes-based deployment and scaling for containerized applications.
• Diagnose and resolve technical issues, system failures, and performance bottlenecks across data and analytics solutions.
• Collaborate with cross-functional teams to troubleshoot and implement solutions.
• Create and maintain documentation for infrastructure, processes, and deployment configurations.
• Ensure documentation is up-to-date and easily accessible to team members.
• Collaborate with writing requirement specifications and design documents for a variety of development tasks including feature development, design specifications and system integrations.
• Collaborate closely with data engineers, data analysts, data scientists, developers, and operations teams to ensure successful deployment and maintenance of data and analytics solutions.
• Communicate effectively with team members, stakeholders, and management about project status, challenges, and solutions.
• Monitor cloud resource usage and optimize costs by right-sizing resources, utilizing reserved instances, and implementing cost-effective strategies.
• Stay updated with the latest Azure services, DevOps best practices, and trends in the data and analytics field.
• Conduct blameless postmortems, find problems, root causes, and solutions to mitigate them. Use failures and the opportunity to learn and improvise the existing system. 

Equal Opportunity Employer: Race, Color, Religion, Sex, Sexual Orientation, Gender Identity, National Origin, Age, Genetic Information, Disability, Protected Veteran Status, or any other legally protected group status.