Job Title: Databricks Platform Administration
Location : Warren, NJ (Hybrid)
Job Type: -Contract
Role Scope
To set clear expectations about how this position divides from adjacent teams:
Production ETL/ELT pipeline development and Spark transformation work sit with the data engineering team.
Model development, training, and tuning sit with the data science team.
Dimensional modeling, dbt development, and BI/semantic layer work sit outside this role.
Day-to-day Databricks administration is handled by a dedicated team; this role provides the standards, architecture, and senior technical partnership behind it.
Required Qualifications
Experience: 6+ years in cloud infrastructure or platform engineering, with at least 4 years focused on Microsoft Azure.
Terraform: Expert-level, hands-on. You have authored and maintained production Terraform modules, managed remote state, and run infrastructure as code through CI/CD. This is a core daily skill in this role.
Databricks Administration: Demonstrable hands-on administration experience — account and workspace administration, Unity Catalog, cluster policies, identity federation, and cost governance. You should be able to describe administrative decisions you have made and explain the tradeoffs behind them.
Technical Partnership: A track record as a senior technical resource to adjacent teams, including guiding stakeholders from an initial request to the underlying requirement and building consensus around the recommended approach.
Azure Networking & Security: Deep working knowledge of private endpoints, VNets and hub-spoke design, NSGs, Entra ID, managed identities, RBAC, and Key Vault.
Data Lake Architecture: ADLS Gen2 design and access control at enterprise scale.
Azure Data Factory: Platform-side experience with integration runtimes, managed VNet, credential management, and deployment automation.
Automation & Scripting: Strong Python, plus PowerShell and/or Bash, for platform tooling and automation.
Spark & SQL Literacy: Sufficient working knowledge to diagnose infrastructure and configuration-level performance issues and engage credibly with data teams. Spark development depth is not a requirement for this role.
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