Senior Databricks Solution Architect

About this role

We are seeking a Senior Databricks Architect to design, implement, and optimize enterprise-scale data platforms using the Databricks Lakehouse Platform . The ideal candidate will have deep expertise in Databricks architecture, Apache Spark, Delta Lake, data engineering, cloud platforms, and enterprise data solutions.

Key Responsibilities

• Design and architect Databricks Lakehouse solutions for large-scale enterprise data platforms.
• Lead end-to-end Databricks implementation, including architecture, development, deployment, and optimization.
• Define Databricks workspace architecture, cluster strategies, security models, and operational standards.
• Design and develop scalable data pipelines using Databricks, Apache Spark, PySpark, SQL, and Delta Lake .
• Implement data ingestion frameworks supporting batch and real-time data processing.
• Design Delta Lake architectures including:
• Delta tables
• Medallion architecture (Bronze/Silver/Gold layers)
• Data quality frameworks
• Schema evolution and optimization
• Perform Databricks performance tuning:
• Cluster optimization
• Spark job optimization
• Query tuning
• Cost optimization
• Implement Databricks governance and security using:
• Unity Catalog
• Access controls
• Data lineage
• Audit capabilities
• Lead migration projects from legacy data platforms to Databricks Lakehouse.
• Define best practices for Databricks CI/CD, DevOps, and environment management.
• Collaborate with data engineers, data scientists, analysts, and enterprise architects.
• Provide technical guidance, architecture documentation, and design reviews.

Skills & Experience

• Strong experience in data engineering, big data, or solution architecture.
• Strong hands-on experience with Databricks Platform .
• Expert knowledge of:
• Apache Spark
• PySpark / Scala
• Delta Lake

- Sql

• Data Lakehouse architecture
• ETL/ELT development
• Experience designing and delivering enterprise Databricks solutions.
• Strong experience with cloud platforms:
• Azure Databricks (preferred)
• AWS Databricks
• Google Cloud Databricks
• Experience with:
• Databricks Workflows
• Jobs orchestration
• Notebooks
• Repos
• Cluster management
• Unity Catalog

Preferred Skills

• Databricks Certified Data Engineer Professional / Databricks Certified Architect.
• Experience with:
• MLflow
• Databricks Machine Learning
• Feature Store
• Model deployment
• MLOps
• Knowledge of streaming technologies:
• Structured Streaming
• Kafka
• Experience with data governance, compliance, and enterprise security frameworks.
• Key Competencies
• Strong Databricks architecture and implementation expertise.
• Ability to design scalable and cost-efficient Lakehouse solutions.
• Strong troubleshooting and performance optimization skills.
• Ability to lead technical discussions with enterprise stakeholders.