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# Data Platform Engineer - Databricks

- **Company:** WIDENET CONSULTING, LLC
- **Location:** Seattle, WA, United States
- **Salary:** $187,200.0 - $208,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Microsoft Azure, Data Infrastructure, Data Masking, Data Security, Interoperability, Performance Tuning, Role-Based Access Control, Data Classification, Snowflake, Caching, Data Management, Databricks
- **Published:** September 10, 2026
- **Apply:** https://widenet-consulting.com/openings/7028/#apply-now

## About the Role

* Experience with PHI/PII scanning and data security implementation (masking, minimization).
 * Skilled in table formats such as Delta, Iceberg, and Uniform, with experience in Databricks-Snowflake interoperability.
 * Experience managing MCP servers and RBAC.
 * Experience connecting AI and external tools to a Databricks Lakehouse environment.
 * Experience building Databricks dashboards and applications.
 * Experience optimizing Azure/Databricks performance and caching, ideally for agentic AI workloads., * Experience with Databricks Genie and enabling self-service data platforms.

## Description

Location: This position requires the candidate to work onsite in Seattle, WA, on Tuesdays and Wednesdays, with the rest of the week remote., * Implement PHI/PII scanning using native Databricks features or custom-built solutions.

 * Design and implement data security controls, including data masking and data minimization.
 * Work with modern table formats (Delta, Iceberg, Uniform) and ensure interoperability between Databricks and Snowflake.
 * Build Databricks dashboards to audit data usage and access by sensitivity and data classification.
 * Manage MCP servers and RBAC (role-based access control) across the platform.
 * Connect AI tools and external systems to the Databricks Lakehouse.
 * Support Databricks Genie and enable EDP (enterprise data platform) for self-service use.
 * Develop Databricks applications.
 * Optimize Azure and Databricks access, including performance tuning and caching strategies, to support agentic AI applications.