Technical Architect

Required Skills & Qualifications

• Extensive experience in data architecture or engineering, with significant hands-on experience in Databricks production environments.
• Proven ability to lead the delivery of enterprise-scale solutions from initial requirements gathering to final deployment.
• Expert-level proficiency in Databricks, including Delta Lake, Unity Catalog, and SQL Warehouses.
• Strong programming skills in PySpark, SQL, and Python.
• Proficiency with cloud platforms such as Azure (ADLS, ADF), AWS (S3, Glue), or GCP.
• Experience with data modeling techniques including Dimensional modeling and Medallion architecture.
• Competency in CI/CD tools and version control systems like Git.
  
  

Preferred Skills

• Databricks Certified Data Engineer Professional or Solutions Architect certification.
• Experience with streaming technologies such as Kafka or Spark Structured Streaming.
• Background in integrating data platforms with BI tools like Power BI or Tableau.
  
  

Solution Architect,Azure Databricks,Azure Databricks,Data Engineering,Optimization,Pipeline Management,Azure Data Factory,Architecture,Data Vault

We are seeking a Data Architect with extensive expertise in the Databricks Lakehouse Platform to lead the design, development, and governance of our enterprise data architecture. The successful candidate will combine technical proficiency in Spark and Delta Lake with strategic leadership to build scalable, production-ready solutions that drive business value. We value a collaborative approach to solving complex problems and provide a supportive environment for technical innovation.

Key Responsibilities

• Design and implement enterprise-grade data architectures using Databricks Lakehouse (Delta Lake, Unity Catalog, Delta Live Tables).
• Lead architectural decisions from conceptual design through to production rollout, ensuring scalability and security are integrated by design.
• Develop comprehensive architecture diagrams, data flow designs, and solution blueprints for stakeholder review.
• Define data platform strategies and best practices across ingestion, storage, and consumption layers.
• Optimize Databricks cluster configurations, workflows, and cost-governance through automated policies.
• Establish data governance and access controls using Unity Catalog.
• Implement CI/CD pipelines and Infrastructure-as-Code using Terraform and Databricks Asset Bundles.
• Mentor data engineering teams on Spark optimization and performance tuning.
• Collaborate with BI and analytics teams to ensure data models support self-service reporting and machine learning.