Data Architect- Databricks

Job Title: Data Architect – Databricks
Company: V4C.ai
Type: Full-time
Experience Level: Senior
We're seeking a skilled Data Architect with hands-on Databricks experience to help build robust pipelines and support scalable analytics and machine learning initiatives.
Key Responsibilities

• Databricks Solution Architecture: Design and implement scalable, secure, and efficient Databricks solutions that meet client requirements.
• Data Engineering: Develop data pipelines, architect data lakes, and implement data warehousing solutions using Databricks.
• Data Analytics: Collaborate with data scientists and analysts to develop and deploy machine learning models and analytics solutions on Databricks.
• Performance Optimization: Optimize Databricks cluster performance, ensuring efficient resource utilization and cost-effectiveness.
• Security and Governance: Implement Databricks security features, ensure data governance, and maintain compliance with industry regulations.
• Client Engagement: Work closely with clients to understand their business requirements, provide technical guidance, and deliver high-quality Databricks solutions.
• Thought Leadership: Stay up to date with the latest Databricks features, best practices, and industry trends, and share knowledge with the team.

Requirements

• Databricks Experience: 5+ years of experience working with Databricks, including platform architecture, data engineering, and data analytics.
• Technical Skills: Proficiency in languages such as Python, Scala, or Java, and experience with Databricks APIs, Spark, and Delta Lake.
• Data Engineering: Strong background in data engineering, including data warehousing, ETL, and data governance.
• Leadership: Proven experience leading technical teams, mentoring junior engineers, and driving technical initiatives.
• Communication: Excellent communication and interpersonal skills, with the ability to work effectively with clients and internal stakeholders.

Good to Have

• Certifications: Databricks Certified Professional or similar certifications.
• Cloud Experience: Experience working with cloud platforms such as AWS, Azure, or GCP.
• Machine Learning: Knowledge of machine learning concepts and experience with popular ML libraries