Senior Databricks Solution Architect

About the role

We are looking for a Senior Databricks Solution Architect to lead the design, implementation, and optimisation of enterprise‑scale data platforms built on the Databricks Lakehouse. The role involves shaping architecture, guiding development teams, and ensuring performance, security and cost‑efficiency across cloud environments.

Key responsibilities

• Design and architect Databricks Lakehouse solutions for large‑scale enterprise data platforms.
• Lead end‑to‑end implementation, including architecture, development, deployment and optimisation.
• Define workspace architecture, cluster strategies, security models and operational standards.
• Build scalable data pipelines using Databricks, Apache Spark, PySpark, SQL and Delta Lake.
• Implement data ingestion frameworks for batch and real‑time processing, including Medallion (Bronze/Silver/Gold) architectures.
• Perform performance tuning, cost optimisation and query optimisation for Spark jobs.
• Implement governance and security using Unity Catalog, access controls, data lineage and audit capabilities.
• Lead migration projects from legacy platforms to Databricks Lakehouse.
• Define CI/CD, DevOps and environment‑management best practices for Databricks.
• Collaborate with data engineers, scientists, analysts and enterprise architects, providing technical guidance and documentation.

Required profile

• Strong experience in data engineering, big data or solution architecture.
• Hands‑on expertise with the Databricks platform and Lakehouse concepts.
• Proven ability to design scalable, cost‑efficient solutions and lead technical discussions with enterprise stakeholders.
• Experience delivering enterprise‑grade Databricks implementations on Azure (preferred), AWS or Google Cloud.
• Solid understanding of data governance, compliance and security frameworks.

Required skills

• Databricks Platform
• Apache Spark
• PySpark / Scala
• Delta Lake
• SQL
• Azure Databricks, AWS Databricks, Google Cloud Databricks
• Databricks Workflows, Jobs orchestration, Notebooks, Repos, Cluster management, Unity Catalog
• MLflow, Databricks Machine Learning, Feature Store, Model deployment, MLOps
• Structured Streaming, Kafka