Systems Engineer - Mid

Role: Systems Engineer - Mid 

Tasks/Responsibilities

• Manage and optimize Snowflake and Databricks environments, including performance tuning, cost controls, workload isolation, and scaling; provide secondary support for Azure SQL and Azure Data Factory.

• Build and maintain data pipelines using Databricks, Snowflake Streams/Tasks, and Azure Data Factory.

• Enforce security and governance controls including RBAC, encryption, auditing, masking, lineage, and Entra ID (Azure AD) integration.

• Tune queries and workloads using native tools such as Snowflake Query Profile, Databricks SQL Analytics, and Azure Monitor.

• Implement and maintain disaster recovery, replication, backup, and archival strategies across platforms.

• Collaborate with data engineers, data scientists, BI teams, and architects to support scalable data models, integrations, and reporting.

• Provide production support, incident response, and root-cause analysis for data platform issues.

• Evaluate and adopt new platform features (e.g., Snowflake Native Apps, Databricks Unity Catalog) to improve governance and automation.

Required Skills/Experience

• Hands-on Snowflake administration: warehouse management, query tuning, clustering, caching, cost governance, Time Travel, and replication.

• Hands-on Databricks administration: workspaces, clusters, SQL Warehouses, jobs, policies, and workload isolation.

• Experience with Databricks Unity Catalog, data governance, lineage, and access controls.

• Proficiency in SQL and scripting with Python and/or PowerShell.

• Experience with Azure data services (Data Factory, Databricks, Azure SQL), including connectivity, authentication, and monitoring.

• Strong understanding of platform security: RBAC, encryption, auditing, masking, and Entra ID integration.

• Experience with backup, recovery, and DR strategies including Snowflake replication and Time Travel.

• Proven production support skills with ability to resolve complex, cross-platform incidents.

• Familiarity with CI/CD and DevOps practices for data platform deployments.

• Solid documentation skills — SOPs, runbooks, architecture diagrams, and DR plans.

• Clear communicator who works well with both technical and non-technical stakeholders.

• Awareness of AI services relevant to platform administration.

Education

• BS in Computer Science, Engineering, Information Systems, or equivalent practical experience (typically 5-7+ years) in data platforms, database administration, or software engineering.