Databricks Architect

Responsibilities:
•       Lead the design, implementation, and optimization of the Databricks platform.
•       Develop and maintain an optimized platform for performance, scalability, and reliability.
•       Collaborate with cross-functional teams to understand business requirements and provide technical solutions.
•       Design and implement data pipelines, ETL processes, and data models.
•       Implement security measures to ensure data privacy and compliance.
•       Troubleshoot and resolve performance issues, system errors, and data inconsistencies.
•       Manage the Team and provide guidance and mentorship to junior team members.

Key Skill Sets:
•       Strong experience with Databricks platform and related technologies.
•       Proficient in programming languages like Python, Scala, or SQL.
•       In-depth knowledge of Apache Spark and its ecosystem.
•       Familiarity with cloud platforms like AWS or Azure.
•       Strong understanding of data engineering principles and best practices.
•       Experience with data modeling, data warehousing, and data integration techniques.
•       Excellent problem-solving and analytical skills.
•       Good communication and collaboration skills.
•       Experience with Snowflake data platform (good to have).
Minimum Qualifications:
•       Bachelor's degree in Computer Science, Engineering, or a related field.
•       Minimum of 5 years of experience in data engineering or architecture roles.
•       Hands-on experience with Databricks platform and Apache Spark.
Preferred Qualifications:
•       Master's degree in Computer Science, Engineering, or a related field.
•       Experience with healthcare or medical device industry.
•       Certifications in Databricks or related technologies.
•       Knowledge of data governance and data quality frameworks.
•       Knowledge of data governance and data quality frameworks.
Focus Areas:
•       Design and optimization of Databricks platform.
•       Performance, scalability, and reliability of the platform.
•       Team management and leadership.
•       Data pipelines, ETL processes, and data modeling.
•       Security and compliance measures.
•       Troubleshooting and issue resolution.
•       Mentoring and team collaboration.