Job Description

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.