Work on Platforms That Matter

About the Role

You'll be embedded in client data teams—designing and building scalable Snowflake-based data platforms. The work spans architecture decisions, hands-on development, and collaborating with client engineers to raise the quality bar. Expect varied clients, real problems, and no repetitive ticket-churning.

What You'll Do

• Design and implement Snowflake data warehouses and data vault / dimensional models
• Build and maintain ELT pipelines using dbt, Airflow, or similar orchestration tools
• Optimize query performance and warehouse costs through clustering, materialization strategies, and resource monitoring
• Implement data quality frameworks and testing strategies
• Advise clients on best practices around RBAC, data governance, and platform architecture
• Participate in architecture reviews and technical discussions with client stakeholders

Requirements

• 4+ years of hands-on experience with Snowflake in production environments
• Strong SQL skills and deep understanding of Snowflake-specific features (time travel, zero-copy cloning, streams, tasks)
• Experience with dbt for transformation layer development and testing
• Proficiency with Python for data engineering tasks and scripting
• Familiarity with data modeling approaches (dimensional, data vault, or medallion architecture)
• Experience with a cloud data platform (AWS, Azure, or GCP)
• Ability to communicate technical concepts clearly to non-technical stakeholders
• Professional proficiency in English

About the Role

You'll work directly with enterprise clients building and optimizing Databricks-based lakehouse platforms. The role covers end-to-end platform work—from ingestion and transformation to governance and performance tuning. You'll be expected to own technical decisions and push quality standards within client teams.

What You'll Do

• Design and implement lakehouse architectures using Databricks, Delta Lake, and Unity Catalog
• Build scalable batch and streaming data pipelines using PySpark and Spark SQL
• Implement medallion (bronze / silver / gold) architecture and enforce data quality at each layer
• Tune Spark jobs for performance and cluster cost efficiency
• Integrate Databricks workflows with orchestration tools such as Airflow or Databricks Workflows
• Advise clients on Unity Catalog setup, data governance, and access control patterns

Requirements

• 4+ years of experience with Databricks or Apache Spark in production environments
• Strong proficiency in PySpark and Python for data pipeline development
• Hands-on experience with Delta Lake (ACID transactions, schema evolution, time travel)
• Experience with Databricks Workflows, Job Clusters, and Unity Catalog
• Solid understanding of data modeling and lakehouse architecture patterns
• Experience deploying Databricks workspaces on at least one major cloud (AWS, Azure, or GCP)
• Good understanding of CI/CD practices for data pipelines (Terraform, GitHub Actions, or similar)
• Professional proficiency in English