Senior Databricks Solution Architect

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.

• 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 developscalable data pipelines using
  Databricks, Apache Spark, PySpark, SQL, and Delta Lake
  .
• Implement data ingestion frameworks supporting batchand real-timedata 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, Dev Ops, and environment management.
• Collaborate with data engineers, data scientists, analysts, and enterprise architects.
• Provide technical guidance, architecture documentation, and design reviews.

• 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 withcloud platforms:
• Azure Databricks (preferred)
• AWS Databricks
• Google Cloud Databricks
• Experience with:
• Databricks Workflows
• Jobs orchestration
• Notebooks
• Repos
• Cluster management
• Unity Catalog

• 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.

• Strong Databricks architecture and implementation expertise.
• Ability to designscalableand cost-efficient
  
  Lakehouse solutions.
• Strong troubleshooting and performance optimization skills.
• Ability to lead technical discussions with enterprise stakeholders.