Databricks Solution Architect
Job SummaryWe are seeking an experienced Databricks Solution Architect to design, architect, and lead the implementation of scalable data and analytics solutions using the Databricks Lakehouse Platform. The ideal candidate will have strong expertise in Databricks, Apache Spark, cloud data platforms, data engineering, data architecture, and modern data lakehouse patterns.The role will work closely with business stakeholders, data engineers, developers, cloud architects, and data scientists to translate business requirements into secure, scalable, and high-performing data solutions.Key ResponsibilitiesDesign and implement end-to-end Databricks Lakehouse architectures for enterprise data and analytics workloads.Develop architecture patterns using Databricks, Delta Lake, Apache Spark, Unity Catalog, and related services.Lead the migration of traditional data warehouses, data lakes, and ETL platforms to Databricks.Design scalable data pipelines for batch and streaming data processing.Define data ingestion, transformation, storage, governance, and consumption strategies.Architect solutions across cloud platforms such as AWS, Azure, or Google Cloud.Establish best practices for data security, access control, governance, lineage, and compliance.Optimize Databricks workloads for performance, scalability, reliability, and cost efficiency.Collaborate with data engineering teams to develop reusable frameworks and implementation standards.Design and implement CI/CD, DevOps, and infrastructure-as-code practices for Databricks environments.Provide technical leadership and guidance to data engineers, developers, and other technical teams.Evaluate new Databricks capabilities and recommend appropriate technologies and architectural approaches.Create architecture diagrams, technical documentation, standards, and solution designs.Troubleshoot complex data platform and integration issues and provide architectural recommendations.Participate in technical discussions, proof-of-concepts, design reviews, and stakeholder presentations.Required Skills & Experience8+ years of experience in data engineering, data architecture, or solution architecture.3+ years of hands-on experience with Databricks and the Lakehouse architecture.Strong experience with Apache Spark, PySpark, SQL, and Delta Lake.Experience designing and implementing enterprise-scale data platforms.Strong understanding of ETL/ELT, data warehousing, data lakes, and lakehouse architectures.Experience with at least one major cloud platform: Azure, AWS, or GCP.Strong knowledge of cloud data services and integration technologies.Experience with Databricks Unity Catalog, data governance, security, and access management.Experience with batch and real-time/streaming data processing.Strong understanding of data modeling, dimensional modeling, and data architecture principles.Experience with CI/CD and DevOps tools and practices.Strong knowledge of Git and infrastructure/configuration management.Excellent problem-solving, communication, documentation, and stakeholder management skills.Preferred QualificationsDatabricks certifications such as Databricks Certified Data Engineer or Databricks Certified Machine Learning Professional.Experience with Azure Data Factory, AWS Glue, AWS S3, Azure Data Lake Storage, Snowflake, Kafka, Airflow, or similar technologies.Experience with Terraform or other Infrastructure-as-Code technologies.Knowledge of data governance and cataloging tools.Experience with machine learning and AI workloads on Databricks.Experience designing highly available and disaster-recovery-ready data platforms.Experience leading enterprise Databricks implementations or cloud migration initiatives.