Data & Analytics DevOps Engineer

Responsibilities:

• Build and maintain Azure DevOps CI/CD pipelines for Data Engineering, dbt, Snowflake, and analytics workloads.
• Support deployment and promotion of data models, database objects, pipelines, and configurations across development, test, and production environments.
• Provide day-to-day operational support for Snowflake , including environment configuration, maintenance, monitoring, performance, access, and troubleshooting.
• Support Fivetran connectors and data integration processes, including monitoring failures, schema changes, and data movement.
• Support DevOps and operational processes for Power BI , including deployment pipelines, workspaces, semantic models, gateways, connections, and refresh monitoring.
• Automate recurring platform administration, deployment, and support activities using scripting, APIs, and workflow automation.
• Support data catalog administration, security, governance, and platform connectivity.
• Assist with integrations to AI and automation technologies such as n8n, Snowflake MCP, and related tools .
• Maintain deployment standards, operational documentation, and support runbooks.
• Partner with Data Engineering, BI, Infrastructure, Security, and Data Science teams to improve platform reliability and operational efficiency.
• Assist with Data Science and ML deployment processes as those capabilities mature.

Qualifications:

• 3–5 years of experience in DevOps, Data Engineering, platform engineering, analytics engineering, or a related role.
• Hands-on experience with Azure DevOps, Git, CI/CD pipelines, and release processes .
• Working knowledge of Snowflake or a comparable cloud data platform.
• Experience with SQL and deploying data or analytics solutions across multiple environments.
• Familiarity with dbt or similar data transformation and modeling technologies.
• Experience with scripting or automation using Python, PowerShell, SQL, APIs, or similar tools .
• Strong troubleshooting and problem-solving skills.
• Ability to work effectively across Data Engineering, BI, and infrastructure teams.