Architect and Design Solutions:
Lead the architecture and design of Databricks-based data solutions that support data engineering, machine learning, and real-time analytics.
Data Pipeline Design:
Design and implement ETL (Extract, Transform, Load) pipelines using Databricks, Apache Spark, and other big data tools to process and integrate large-scale data from multiple sources.
Collaborate with Stakeholders:
Work with business and data teams to understand requirements, identify opportunities for automation, and design solutions that improve data workflows.
Optimize Data Architecture:
Create highly optimized, scalable, and cost-effective architectures for processing large data sets and managing big data workloads using Databricks, Delta Lake, and Apache Spark.
Implement Best Practices:
Define and promote best practices for Databricks implementation, including data governance, security, performance optimization, and monitoring.
Manage Databricks Clusters:
Manage and optimize Databricks clusters for performance, cost, and reliability. Troubleshoot performance issues and optimize the use of cloud resources.
Data Governance and Security:
Implement best practices for data governance, security, and compliance on the Databricks platform to ensure that data processing and storage meet organizational and regulatory standards.
Automation and Optimization:
Automate repetitive tasks, streamline data processes, and optimize data workflows to improve efficiency and reduce operational costs.
Mentorship and Training:
Mentor and provide guidance to junior engineers, ensuring the team follows best practices in the development of data pipelines and analytics solutions.
Keep Up-to-Date with Trends:
Stay current with emerging technologies in the big data and cloud space, and recommend new solutions or improvements to existing processes.