Databricks Solution Architect

• Design end-to-end data engineering and analytics solutions using Databricks Lakehouse architecture
• Define scalable architectures for ingestion, transformation, storage, orchestration, analytics, and downstream consumption
• Develop architecture patterns using Apache Spark, PySpark, SQL, Delta Lake, Unity Catalog, and Databricks services
• Design and guide batch and streaming pipelines, ETL/ELT frameworks, data models, and enterprise data integration solutions
• Evaluate data platforms and recommend migration, modernization, and performance optimization strategies
• Establish architecture standards, reusable design patterns, engineering best practices, and implementation guidelines
• Guide data engineering teams on technical design, code quality, scalability, reliability, and performance tuning
• Participate in POCs, architecture reviews, technical troubleshooting, and production deployments
• Engage clients to understand business objectives, data ecosystems, technical challenges, and requirements
• Lead technical discovery sessions, requirements workshops, and solution architecture discussions
• Translate business requirements into technical solutions, architecture diagrams, implementation approaches, and delivery roadmaps
• Prepare technical proposals, solution presentations, RFP/RFI responses, and Statements of Work with sales and delivery teams
• Present architecture recommendations, technology choices, implementation strategies, assumptions, dependencies, and risks
• Assess technical feasibility and recommend technologies based on scalability, security, and cost
• Support solution handover from presales to implementation teams
• Estimate project effort, timelines, resource requirements, implementation costs, Databricks consumption, and cloud infrastructure costs
• Determine team composition and resource allocation across architecture, data engineering, cloud engineering, QA, and related functions
• Identify cost drivers including DBUs, compute sizing, storage, networking, frequency, concurrency, and processing requirements
• Identify cost optimization opportunities and provide technical and commercial inputs for pricing and budgeting
• Design and implement Databricks solutions on Azure, AWS, or GCP
• Evaluate cloud-native services for ingestion, storage, orchestration, security, monitoring, and integration
• Define data access, security, governance, and compliance considerations
• Optimize compute utilization, workload execution, storage consumption, and cloud spending
• Support cloud data platform migrations and modernization initiatives
• Review technical designs and implementation plans for architecture and business alignment
• Identify risks, dependencies, and implementation challenges and recommend mitigations
• Mentor engineering teams on Databricks development, cloud architecture, and performance optimization
• Coordinate client stakeholders, presales teams, architects, and engineering teams throughout the project lifecycle

• Minimum 7+ years of experience
• 8–12+ years of overall experience in Data Engineering, Data Architecture, Cloud Data Solutions, or related disciplines
• Strong hands-on experience designing and implementing enterprise-grade solutions on Databricks
• Deep understanding of Lakehouse principles, Delta Lake, data storage, processing, governance, and analytics architecture
• Strong expertise in Apache Spark, PySpark, SQL, distributed processing, and performance tuning
• Experience designing scalable ETL/ELT pipelines, data models, and data integration frameworks
• Strong understanding of Unity Catalog, data platform architecture, and enterprise data engineering practices
• Hands-on experience with at least one major cloud ecosystem: Azure, AWS, or GCP
• Experience with cloud data services, orchestration frameworks, APIs, and enterprise data platforms
• Ability to estimate project effort, resource requirements, implementation timelines, and platform infrastructure costs
• Experience with client-facing technical consulting, solution architecture, presales, and proposal development
• Ability to create architecture diagrams, technical proposals, implementation roadmaps, and cost estimates
• Strong analytical, presentation, stakeholder-management, and problem-solving skills
• Experience defining data governance frameworks, data ownership, cataloging, lineage, and governance policies
• Understanding of role-based access control, identity management, permissions, and secure data access
• Knowledge of data protection, encryption, access policies, regulatory compliance, and cloud security best practices
• Exposure to enterprise data migration, legacy platform modernization, and large-scale data transformation programs
• Familiarity with machine learning, Generative AI, and Databricks AI capabilities
• Experience with consulting firms, system integrators, cloud service providers, or data engineering service organizations
• Knowledge of cloud pricing models, platform consumption metrics, FinOps principles, and infrastructure cost optimization
• Relevant Databricks or cloud certifications are preferred
• Candidates should provide total professional experience and relevant Databricks experience, cloud expertise, delivered data solutions, presales and proposal experience, estimation and cost-calculation experience, certifications, notice period, and availability