Databricks Architect

2 days ago

Multiple Locations, India BL Consultants Full time ₹ 2,00,00,000 - ₹ 4,00,00,000 per year

Databricks Architect :

Key Responsibilities :

1. Databricks Solution Architecture: Design and implement scalable, secure, and efficient Databricks solutions that meet client requirements.

2. Data Engineering: Develop data pipelines, architect data lakes, and implement data warehousing solutions using Databricks.

3. Data Analytics: Collaborate with data scientists and analysts to develop and deploy machine learning models and analytics solutions on Databricks.

4. Performance Optimization: Optimize Databricks cluster performance, ensuring efficient resource utilization and cost-effectiveness.

5. Security and Governance: Implement Databricks security features, ensure data governance, and maintain compliance with industry regulations.

6. Client Engagement: Work closely with clients to understand their business requirements, provide technical guidance, and deliver high-quality Databricks solutions.

7. Thought Leadership: Stay up-to-date with the latest Databricks features, best practices, and industry trends, and share knowledge with the team.

Requirements :

1. Databricks Experience: 5+ years of experience working with Databricks, including platform architecture, data engineering, and data analytics.

2. Technical Skills: Proficiency in languages such as Python, Scala, or Java, and experience with Databricks APIs, Spark, and Delta Lake.

3. Data Engineering: Strong background in data engineering, including data warehousing, ETL, and data governance.

4. Leadership: Proven experience leading technical teams, mentoring junior engineers, and driving technical initiatives.

5. Communication: Excellent communication and interpersonal skills, with the ability to work effectively with clients and internal stakeholders.

Good to Have :

1. Certifications: Databricks Certified Professional or similar certifications.

2. Cloud Experience: Experience working with cloud platforms such as AWS, Azure, or GCP.

3. Machine Learning: Knowledge of machine learning concepts and experience with popular ML libraries.

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