Databricks Archtiect

Job Summary

We are seeking a highly experienced Data Architect with expertise in Databricks, modern lakehouse architectures, cloud data platforms, and enterprise data strategy. The ideal candidate will define and implement scalable, secure, high-performance data architectures that support enterprise reporting, advanced analytics, AI, and machine learning initiatives.

The Data Architect will collaborate with business stakeholders, data engineering teams, cloud architects, and technology leadership to translate business requirements into robust data architecture and implementation roadmaps.

Key Responsibilities

• Define enterprise data architecture and technical strategies using Databricks and cloud-native data platforms.
  
• Design scalable lakehouse solutions using Delta Lake, medallion architecture, and appropriate data storage patterns.
  
• Establish architectural standards for data ingestion, transformation, integration, modeling, and consumption.
  
• Design batch and streaming data processing architectures using Apache Spark, PySpark, and SQL.
  
• Define data modeling standards, including conceptual, logical, and physical data models.
  
• Establish data governance, metadata management, lineage, data quality, and access-control frameworks.
  
• Implement governance and security strategies using Unity Catalog and cloud-native identity and security services.
  
• Evaluate and recommend cloud services, data technologies, and integration patterns.
  
• Develop architectural blueprints, reference architectures, technical documentation, and implementation roadmaps.
  
• Guide senior engineers and development teams on architectural decisions and engineering best practices.
  
• Optimize platform performance, scalability, reliability, and overall cloud and Databricks costs.
  
• Collaborate with enterprise architects, security teams, business stakeholders, and data platform teams.
  

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Requirements

Required Skills

• 10+ years of experience in data architecture, data engineering, or enterprise data platforms.
  
• Strong hands-on experience with Databricks and cloud-based data architectures.
  
• Deep understanding of lakehouse architecture, Delta Lake, Apache Spark, and distributed data processing.
  
• Strong knowledge of Python, PySpark, SQL, ETL/ELT patterns, and data integration.
  
• Experience designing data platforms on AWS, Microsoft Azure, or Google Cloud.
  
• Expertise in enterprise data modeling, data warehousing, and data platform design.
  
• Experience with data governance, security, metadata management, and data lifecycle management.
  
• Ability to define architecture standards, evaluate technology options, and lead technical design discussions.
  
• Strong communication skills and the ability to translate business requirements into technical architecture.