Data and 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.