Databricks Solution Architect

Weekday (YC W21)

. Design end-to-end data engineering and analytics solutions using Databricks Lakehouse architecture .

Posted 10/23/2026full-timeRemote • IndiaSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing scalable data engineering solutions using Databricks, Apache Spark, and cloud platforms such as Azure, AWS, or GCP. Proficient in data governance, architecture standards, and client engagement for technical consulting and solution delivery.

Highest-signal resume keywords

Databricks Solution DesignApache Spark ExpertiseCloud Data SolutionsETL/ELT Pipeline DevelopmentData Governance Frameworks

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

DatabricksApache SparkPySparkSQLDelta LakeUnity CatalogETL/ELT FrameworksData ModelingCloud ArchitecturePerformance Tuning

Soft Skills

Analytical SkillsPresentation SkillsStakeholder ManagementProblem-Solving Skills

Tools & Technologies

AzureAWSGCPData PlatformsOrchestration FrameworksAPIs

Certifications & Qualifications

Databricks CertificationsCloud Certifications

Industry Keywords

Data EngineeringData ArchitectureCloud Data SolutionsData GovernanceData MigrationLegacy Platform ModernizationFinOps Principles

Tech Stack

Tools & technologies

ApacheAWSAzureCloudETLGoogle Cloud PlatformPySparkSparkSQLUnity

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

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