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
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