About the role
Job Description: Databricks Solution Architect — Presales & Delivery
Experience: 8–12+ years of overall experience in Data Engineering, Data Architecture, or Cloud Data Solutions
Employment Type: Full-time / Contract
Work Model: As per project requirements
Technical Focus: Databricks, Lakehouse Architecture, Cloud Data Platforms, Presales & Cost Estimation
Role Overview
We are looking for an experienced Databricks Solution Architect who can bridge the gap between client requirements, solution architecture, commercial estimation, and technical implementation.
The ideal candidate will be responsible for understanding business and technical requirements, designing scalable and cost-efficient Databricks solutions, estimating project effort and infrastructure costs, supporting presales activities, and guiding engineering teams through implementation and deployment.
This role requires a combination of strong technical expertise, client-facing consulting skills, commercial awareness, and hands-on delivery experience. The candidate should be comfortable presenting solutions to clients as well as getting involved in technical design reviews, proof of concepts (POCs), and implementation challenges.
Key Responsibilities
1. Solution Architecture & Technical Delivery
Design end-to-end data engineering and analytics solutions using Databricks Lakehouse architecture.
Define architecture for data ingestion, transformation, storage, orchestration, analytics, and downstream data consumption.
Develop architecture patterns using Apache Spark, PySpark, SQL, Delta Lake, Unity Catalog, and relevant Databricks services.
Design and guide batch and streaming data pipelines, data models, and data integration frameworks.
Evaluate existing data platforms and recommend modernization, migration, and optimization strategies.
Guide engineering teams on architecture implementation, technical standards, performance optimization, and best practices.
Participate in POCs, technical reviews, troubleshooting, and production deployment when required.
2. Presales & Client Engagement
Engage with clients to understand business objectives, existing data ecosystems, technical challenges, and project requirements.
Lead technical discovery sessions, requirement-gathering workshops, and architecture discussions.
Translate client requirements into technical solutions, architecture diagrams, implementation approaches, and delivery roadmaps.
Prepare technical proposals, solution presentations, RFP/RFI responses, and statements of work in collaboration with relevant teams.
Present proposed solutions, architectural trade-offs, assumptions, risks, and implementation approaches to client stakeholders.
Support presales discussions by assessing feasibility, identifying dependencies, and recommending suitable technology choices.
Collaborate with sales, business development, delivery, and engineering teams to develop technically sound and commercially viable proposals.
3. Project Costing & Estimation
Estimate project effort, delivery timelines, resource requirements, and implementation costs based on client requirements.
Break down project scope into phases, work packages, deliverables, milestones, and engineering activities.
Estimate the required team composition, including architects, data engineers, cloud engineers, QA resources, and other specialists as applicable.
Calculate estimated Databricks platform consumption and cloud infrastructure costs based on workload requirements and architectural design.
Assess compute requirements, DBU consumption, storage, networking, workload frequency, concurrency, and other relevant cost drivers.
Prepare high-level and detailed estimates for implementation, migration, platform modernization, and ongoing support engagements.
Identify cost optimization opportunities and recommend suitable architecture and compute-sizing options.
Document assumptions, dependencies, exclusions, risks, contingency, and potential cost variations.
Support commercial pricing discussions by providing effort estimates, delivery costs, and technical inputs to project proposals.
4. Cloud Architecture & Cost Optimization
Design Databricks solutions on Azure, AWS, or GCP, depending on project requirements.
Evaluate cloud-native services for data ingestion, storage, orchestration, security, monitoring, and integration.
Implement architectural recommendations for scalability, reliability, security, governance, and performance.
Identify opportunities to optimize compute utilization, workload execution, storage, and overall platform spending.
Ensure proposed architectures align with relevant security, compliance, and data governance requirements.
Required Technical Skills
Strong hands-on experience with Databricks architecture and implementation.
Deep understanding of Lakehouse architecture, Delta Lake, Apache Spark, PySpark, and SQL.
Experience with Unity Catalog, data governance, access controls, and data security.
Expertise in designing scalable ETL/ELT pipelines, data models, and data integration solutions.
Strong knowledge of at least one cloud ecosystem: Microsoft Azure, AWS, or Google Cloud Platform.
Experience with cloud data services, orchestration, APIs, and enterprise data platforms.
Understanding of workload sizing, performance tuning, and cloud/platform cost optimization.
Ability to create architecture diagrams, technical proposals, solution designs, and implementation plans.
Required Presales & Commercial Skills
Demonstrated experience in client-facing technical consulting or solution architecture.
Experience leading requirement-discovery workshops and translating business needs into technical solutions.
Proven ability to estimate project effort, resource requirements, timelines, and implementation costs.
Understanding of Databricks and cloud infrastructure pricing and consumption-based cost models.
Experience preparing technical proposals, estimates, RFP/RFI responses, or statements of work.
Ability to communicate technical concepts, architectural trade-offs, and cost implications to both technical and non-technical stakeholders.
Strong analytical, presentation, stakeholder-management, and problem-solving skills.
Preferred Qualifications
Databricks certifications such as Databricks Certified Data Engineer Professional, Data Engineer Associate, or relevant architecture-focused credentials.
Experience with data platform migration, legacy modernization, and enterprise-scale data transformation programs.
Exposure to machine learning, Generative AI, or Databricks AI capabilities.
Experience working with consulting firms, system integrators, cloud service providers, or data engineering service companies.
Experience supporting client proposals from initial discovery through solution design, estimation, and delivery handover.
Ideal Candidate Profile
The ideal candidate is a technically strong Databricks Solution Architect who can independently engage with clients, understand complex requirements, design the right solution, estimate implementation and operating costs, support presales activities, and guide the engineering team through delivery.
The candidate must be capable of both winning technical confidence during presales and validating the proposed solution through hands-on technical involvement. This is not a pure presales or presentation-only role.
Application Requirements
Interested candidates should share their updated resume highlighting:
Total experience and relevant Databricks experience.
Cloud platform expertise.
Examples of end-to-end solutions architected and delivered.
Presales, client engagement, and proposal-development experience.
Experience with project estimation, resource planning, and cloud/platform cost calculations.
Certifications, notice period, and availability.
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