Design and architect scalable Databricks-based solutions for enterprise clients.
Lead end-to-end cloud data platform implementations across AWS and/or Azure.
Design and build production pipelines using Databricks Workflows and Spark Declarative Pipelines.
Design scalable ETL/ELT pipelines using Spark, PySpark, and Databricks Workflows.
Partner directly with client stakeholders to understand business needs and translate them into technical solutions.
Provide technical leadership and guidance to data engineering teams.
Implement CI/CD pipelines and infrastructure-as-code for Databricks resources.
Drive collaborative development through Git workflows, pull requests, and code reviews.
Collaborate cross-functionally with analytics, engineering, and business teams.
Serve as the primary technical point of contact for clients, including C-level executives.

6+ years of experience in Data Engineering, Cloud Architecture, or related roles.
3+ years of hands-on experience with Databricks.
Strong proficiency in PySpark, Spark SQL, Python, and SQL.
Deep experience with Delta Lake, Unity Catalog, Delta Live Tables, and Databricks Jobs.
Proven experience delivering Databricks projects in a consulting or professional services environment.
Strong understanding of data lake concepts and formats (Delta, Iceberg, Parquet, etc.).
Experience designing and managing Unity Catalog for governance, access control, and lineage.
Familiarity with medallion architecture (bronze/silver/gold) patterns.
Hands-on experience with Git version control, pull requests, code reviews, and collaborative development workflows.
CI/CD and infrastructure-as-code for Databricks resources (Terraform, Asset Bundles, GitHub Actions).
MLflow experience: experiment tracking, model registry, deployment, and observability.
Cloud platform experience.
Experience working in client-facing, consulting, or cross-functional environments.
Experience leading technical projects or mentoring engineering teams.

Databricks Architect

Key skills