Databricks architect

1.      Data Architecture Design and Implementation:

a.      Design and implement cloud-based data architectures leveraging Databricks, AWS, Azure, or GCP.

b.      Architect end-to-end data pipelines that integrate with Databricks and other cloud services.

1.      Data Integration:

a.      Integrate data from various sources, including operational databases, data lakes, and third-party APIs.

1.      Optimization and Performance Tuning:

a.      Optimize Databricks Spark workloads for performance, cost, and scalability

b.      Monitor and tune Databricks clusters to ensure optimal performance for analytics and data science workflows

1.      Collaboration with Data Teams:

a.      Implement data governance practices to ensure the integrity, security, and compliance of data within Databricks

b.      Ensure that the architecture adheres to industry standards for data security, privacy, and compliance (e.g., GDPR, CCPA).

1.      Security and Governance:

a.      Implement data governance practices to ensure the integrity, security, and compliance of data within Databricks

1.      Cloud Infrastructure and Automation:

a.      Design and manage cloud infrastructure for Databricks, ensuring high availability and fault tolerance.