The Databricks Architect will design and implement data architecture solutions using Databricks, AWS, and Snowflake, focusing on MLOps and data governance.
Tiger Analytics is a fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best analytics global consulting team in the world.
This role will be responsible for Databricks Architecture, Designing and implementing best data practices.
Requirements
- Strong Experience in Data Architecture, specifically with Databricks Architecture.
- Expert in Databricks Lakehouse (Delta Lake, Unity Catalog, MLflow), AWS, Snowflake, and Apache Iceberg.
- Strong MLOps & CI/CD Expertise
- Proficient in Python/Scala (Spark) for data governance, security, and enterprise data platform design.
- Databricks certifications and GenAI architecture experience preferred.
Key Responsibilities:
- Architect and strategize Databricks on AWS/Snowflake integration for secure, scalable data/AI platforms.
- Architect seamless Snowflake/Databricks data flow via Apache Iceberg (including ML output).
- Design cost-managed multi-region Databricks MLOps platform and data flows.
- Implement Unity Catalog for fine-grained access control and secure team coexistence with Workspaces.
- Develop Databricks-native MLOps (environment parity, Git-driven CI/CD, governed access, MLflow governance, standardized deployment, monitoring).
- Define enterprise-scalable AI governance for GenAI production deployment.
Benefits
This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.
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