Databricks Architect

Key Responsibilities

• Design scalable, secure, and high-performance data platforms using Databricks and Lakehouse architecture.
• Lead end-to-end architecture design for data lakes, lakehouses, and data warehouses.
• Define data architecture standards, governance principles, and best practices.
• Design batch and streaming data processing solutions.
• Translate business requirements into technical architectures and implementation roadmaps.
• Provide technical leadership and architectural guidance to data engineering teams.
• Conduct architecture reviews, technical assessments, and client workshops.
• Advise clients on data platform modernization, performance optimization, security, and cost efficiency.

Key Requirements

• 5+ years of experience designing and implementing cloud-based data platforms, ideally in a Data Architect or Senior Data Engineer role.
• Strong hands-on experience with Databricks, Apache Spark, Delta Lake, and Lakehouse architecture.
• Strong SQL and PySpark skills, including performance optimization.
• Experience with Unity Catalog, Databricks Workflows, and Delta Live Tables / Lakeflow Spark Declarative Pipelines.
• Solid understanding of data modeling (Kimball, Inmon, Medallion Architecture) and ETL/ELT principles.
• Experience designing batch and streaming data pipelines.
• Experience with at least one major cloud platform (Azure, AWS, or GCP).
• Knowledge of data governance, security, CI/CD, Infrastructure as Code, and DevOps practices.
• Strong communication skills and the ability to lead technical discussions with clients and delivery teams.

Nice to Have

• Databricks certifications.
• Experience with Azure Databricks and Azure data services.
• Experience with Apache Kafka and Spark Structured Streaming.
• Cloud migration and data platform modernization experience.
• Familiarity with MLflow and AI/ML workloads.
• Previous experience in client-facing consulting roles.