DevOps Engineer

DevOps Engineer [Analytics Platform Engineer]

An Analytics Platform Engineer supporting Azure Synapse and Azure Data Factory from a DevOps perspective requires a hybrid skill set spanning primarily cloud infrastructure management, automation and CI/CD practices and it is helpful if they a background or exposure to ELT and data engineering and Database Administration Concepts etc. The resource will be responsible as an engineer on Analytics Platform Engineering team where we Deploy Synapse Analytics Workspaces and Azure Data Factory and are responsible for the implementation for our internal teams as a PaaS offering and may support a CI/CD solution and using a Custom Gitlab to GitHub adapter/translator written in GoLang.

Core Responsibilities:

• Implement CI/CD Pipelines: Design and maintain continuous integration and continuous deployment pipelines using tools like Azure DevOps or GitHub Actions for deploying data pipelines and infrastructure changes.
• Infrastructure as Code (IaC): Provision and manage Azure data services (Synapse, ADF, Data Lake, etc.) through code using tools such as Terraform, ARM templates, or Bicep to ensure consistency and repeatability.
• Platform Operations: Deploy, manage, and optimize the performance and resource utilization of Azure data services, including monitoring and troubleshooting data pipeline failures and performance issues.
• Automation & Scripting: Automate routine operational tasks and application deployments using scripting languages like Python, PowerShell, or Bash.
• Security & Compliance: Implement security best practices, including identity and access management (IAM), data encryption, and compliance with data governance policies (e.g., GDPR) within the platform.
• Collaboration: Work closely with data engineers, data scientists, and business analysts to translate data requirements into robust technical solutions and foster a DevOps culture within the organization.

Key Skills and Qualifications:

• Cloud Platform Expertise: Deep knowledge of Microsoft Azure services, specifically Azure Synapse Analytics (SQL pools, Spark pools, pipelines), Azure Data Factory (pipelines, triggers, data flows), Azure Data Lake Storage (ADLS), and Azure Monitor.
• DevOps Tools & Methodologies:
• CI/CD platforms: Expertise in Azure DevOps (Azure Pipelines, Boards, Repos) or GitHub Actions.
• Version Control: Strong experience with Git and branching strategies.
• IaC tools: Proficiency in Terraform, Bicep, or ARM templates.
• Containerization: Experience with Docker and container orchestration (Azure Kubernetes Service - AKS) is highly beneficial.
• Programming & Scripting Languages: Proficiency in Python, SQL (T-SQL, query optimization), and scripting languages like PowerShell or Bash for automation and data manipulation tasks.
• Data Engineering Fundamentals: Strong understanding of ETL/ELT processes, data modeling, data warehousing, and big data concepts.
• Monitoring & Logging: Experience implementing monitoring, logging, and alerting solutions using tools like Azure Monitor, Log Analytics, and Application Insights.
• Soft Skills: Excellent problem-solving, analytical, and communication skills to work effectively in fast-paced, agile environments.
• Preferred Certifications: Microsoft Certified: Azure Data Engineer Associate (DP-203) or Microsoft Certified: Azure DevOps Engineer Expert (AZ-400) are highly valued.

Must Have:

• 4+ years of experience developing APIs and integrating with 3rd party APIs.
• Strong SQL skills (SQL Server or Oracle).
• Familiarity with scripting languages (Python, Bash, PowerShell).
• Experience with version control systems (Git, GitHub, GitLab).
• Knowledge of CI/CD pipelines and DevOps best practices.
• Understanding of workflow automations is a plus.
• Excellent problem-solving, analytical, and communication skills.

Azure AD, OAuth, JWT, Microsoft Teams SDK, Building apps for teams, Azure hands with AD B2C, BOT, SignalR, AKS, App Registrations, App InsightsAzure SynapseCI/CD: Jenkins, Terraform, GitLab CI/CD

Nice To Have:

• Data Engineer
• Strong understanding of ETL/ELT processes, data modeling, data warehousing, and big data concepts.