Databricks Architect

Strong architectural judgment and the ability to connect platform design decisions with business and operational needs.

A hands-on approach to engineering, with confidence moving between architecture planning and implementation.

Deep understanding of enterprise Data Governance, secure data sharing, and practical controls for large-scale environments.

Clear communication skills for explaining architecture choices and promoting consistent engineering practices across teams.

Key Responsibilities

Design enterprise-scale Lakehouse solutions using Databricks, including target-state architectures and migration roadmaps.

Develop reference architectures, reusable frameworks, and accelerators that support consistent delivery and platform adoption.

Establish data governance, security, and operational standards across the platform lifecycle.

Implement role-based access controls, data masking, and governance models with Unity Catalog, supporting classification, lineage, auditability, and secure sharing.

Develop and implement PySpark jobs and incorporate orchestration approaches using tools such as Airflow.

Apply advanced platform capabilities, including Databricks Apps and Delta sharing, and support real-time and streaming integration.

Required Qualifications

Demonstrated capability in enterprise data architecture and Databricks platform implementation.

Strong knowledge of Unity Catalog and governance practices for large-scale data environments.

Hands-on experience developing and implementing PySpark workloads.

Background working with orchestration tools such as Airflow.

Knowledge of real-time data integration, streaming patterns, access management, and secure data sharing.

Preferred Qualifications

Familiarity with CI/CD methods for reliable deployment and maintainable data engineering workflows.

Experience applying Azure services within enterprise data platform architectures.

Interest in enabling governed data foundations for Generative AI use cases.