New opportunity

Cloud Solution Architecture IC5

Microsoft · Bangalore, India

About this role

The Customer & Commercial Success Architect’s Office at Microsoft is seeking a Cloud Solution Architect to drive the adoption of Databricks solutions on the Microsoft Azure platform. This role involves partnering with customers and Databricks to achieve business outcomes by leveraging deep technical expertise in data, analytics, and AI services.

Responsibilities

Serve as a trusted technical advisor to Databricks, customers, and partners to realize business value from Microsoft Azure and Databricks investments.
Lead architecture reviews, technical strategy discussions, proof-of-concepts, and production deployments for data, analytics, AI, and modernization solutions.
Guide customers and partners in implementing best practices for security, governance, reliability, performance, and operational excellence.
Identify and resolve technical blockers that impact solution deployment, scalability, and adoption.
Design, validate, and optimize enterprise-scale architectures leveraging Databricks, Azure Data Services, and AI Services.
Develop reusable intellectual property, reference architectures, and deployment accelerators.
Collaborate with cross-functional teams including Sales, Engineering, and Partner Development to drive measurable customer outcomes.

Required qualifications

Bachelor's Degree in Computer Science, Information Technology, Engineering, Business, Liberal Arts, or related field AND 7+ years of experience in cloud/infrastructure technologies, IT consulting, systems administration, or software development.
6+ years of customer-facing technical sales experience.
6+ years of experience leading technical projects.
Deep expertise in Databricks, Apache Spark, Delta Lake, and modern Data & AI platforms including machine learning and Generative AI.
Proven experience designing and deploying enterprise-scale cloud solutions on Microsoft Azure, including Azure Databricks, Microsoft Fabric, and Azure AI Services.
Strong background in building data lakes, data warehouses, and streaming architectures.
Ability to pass the Microsoft Cloud Background Check.

Preferred qualifications

Bachelor's degree with 8+ years of experience or Master's degree with 6+ years of experience in relevant technology fields.
Proficiency in Well-Architected Framework and Cloud Adoption Framework.
Strong skills in situational fluency, relationship building, and architecture design thinking.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

DatabricksApache SparkDelta LakeMicrosoft AzureMicrosoft FabricAzure Data Lake StorageAzure Synapse AnalyticsAzure AI ServicesAzure Machine LearningCloud ArchitectureData EngineeringGenerative AITechnical SalesSolution Architecture

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Driving Databricks Adoption on Azure
The core mandate is to accelerate the consumption and adoption of Databricks solutions within the Microsoft Azure ecosystem. You must be able to articulate the business value of this integration to customers and partners, effectively bridging the gap between Databricks capabilities and Azure platform strengths to meet specific consumption metrics.
Technical Advisory and Architecture Leadership
You are expected to act as a trusted technical advisor, leading architecture reviews and strategy discussions. This requires deep expertise in designing enterprise-scale data, analytics, and AI solutions, ensuring they align with best practices for security, governance, and operational excellence as defined by the Well-Architected and Cloud Adoption Frameworks.
Cross-Functional Collaboration and IP Development
Success depends on your ability to collaborate across Sales, Engineering, and Partner Development teams. You must proactively develop reusable intellectual property, such as reference architectures and deployment accelerators, to improve the scalability and repeatability of solutions across different customer engagements.

Likely challenges to think through

Navigating Complex Stakeholder Ecosystems
You will need to influence diverse stakeholders, including Databricks, Microsoft internal teams, and external customer decision-makers. The challenge lies in aligning these groups, who may have competing priorities, toward a unified technical strategy. Prepare by demonstrating your experience in relationship mapping and managing executive-level expectations.
Resolving Technical Blockers in Production
You are responsible for identifying and removing technical blockers that impede deployment or adoption. This requires rapid diagnostic skills and the ability to troubleshoot complex, large-scale distributed systems. Prepare by being ready to discuss specific instances where you diagnosed a critical failure in a data pipeline or AI workload and implemented a scalable fix.
Balancing Technical Depth with Business Outcomes
The role requires high-level technical expertise while remaining focused on measurable business results and consumption growth. The difficulty is ensuring that architectural decisions do not become purely academic but directly translate into customer value. Prepare by framing your past architectural projects through the lens of business impact and ROI.

Practice your answers

Technical practice

Optimizing Databricks on Azure

What to coverDiscuss how you would optimize a Databricks workload for cost and performance on Azure. Focus on cluster sizing, instance types, auto-scaling, and the effective use of Delta Lake features like Z-Ordering and data skipping.

Implementing Well-Architected Framework

What to coverExplain how you apply the five pillars of the Microsoft Well-Architected Framework to a data platform project. Provide examples of how you balance trade-offs between security, cost optimization, and operational excellence.

Modernizing Data Architectures

What to coverDescribe your approach to migrating legacy on-premises data warehouses to a modern Azure architecture using Databricks and Microsoft Fabric. Address how you handle data ingestion, transformation, and governance during the transition.

Streaming Data Pipelines

What to coverDetail the architecture for a real-time streaming pipeline using Spark Structured Streaming and Azure Event Hubs. Explain how you ensure fault tolerance, exactly-once processing, and low latency.

Generative AI Integration

What to coverHow do you integrate Generative AI capabilities into an existing enterprise data platform? Discuss the role of vector databases, model serving, and ensuring data privacy and security when using Azure AI Services.

Governance and Security in Data Lakes

What to coverWhat strategies do you use to implement robust governance and security in a shared data lake environment? Address access control, data lineage, and compliance requirements using tools like Unity Catalog or Azure Purview.

Troubleshooting Deployment Blockers

What to coverDescribe a scenario where a production deployment was stalled due to a technical blocker. Explain your methodology for identifying the root cause, engaging the right stakeholders, and implementing a resolution.

Designing for Scalability

What to coverWhen designing enterprise-scale solutions, how do you ensure the architecture remains scalable as data volumes grow? Discuss the importance of modular design, decoupling compute from storage, and reusable reference architectures.

Behavioural practice

Building Trusted Advisor Relationships

What to coverDescribe a time you had to establish credibility with a skeptical executive stakeholder. How did you use situational fluency and active listening to understand their business drivers and align your technical recommendations?

Managing Cross-Functional Collaboration

What to coverGive an example of leading a virtual team across different departments like Sales, Engineering, and Partners. How did you align these groups to achieve a common customer outcome despite competing priorities?

Handling Conflicting Requirements

What to coverTell me about a situation where a customer's requested architecture conflicted with best practices for security or performance. How did you navigate this conversation to reach a solution that satisfied the customer while maintaining architectural integrity?

Driving Adoption and Consumption

What to coverDescribe a project where you were responsible for driving the adoption of a new technology. What specific actions did you take to overcome resistance and ensure the customer realized measurable business value?

Sharpen your knowledge

Topics to prepare

Microsoft Well-Architected and Cloud Adoption Frameworks
Review the five pillars of the Well-Architected Framework (Reliability, Security, Cost Optimization, Operational Excellence, Performance Efficiency) and the phases of the Cloud Adoption Framework. Exercise: Map a hypothetical Databricks-on-Azure deployment to these pillars to identify potential gaps in governance or scalability.
Enterprise Data and AI Architecture Patterns
Focus on the integration of Databricks with Azure Data Services, specifically focusing on data lakehouse architectures, streaming ingestion, and Generative AI deployment patterns. Exercise: Draft a reference architecture diagram that connects Azure Data Lake Storage, Databricks, and Azure AI Services to solve a common enterprise data modernization challenge.

Skills to revise

Databricks and Apache Spark Optimization
Deepen your knowledge of performance tuning in Spark, specifically Delta Lake optimization, cluster configuration, and cost management. Exercise: Analyze a scenario where a Spark job is failing or underperforming and document the specific configuration changes or code refactoring needed to improve throughput.
Technical Sales and Situational Fluency
Practice articulating complex technical value propositions to non-technical stakeholders and managing technical blockers during customer engagements. Exercise: Prepare a 5-minute pitch that translates a specific Databricks technical feature into a measurable business outcome for a C-level executive.
Azure Data and AI Ecosystem Integration
Gain hands-on familiarity with the interoperability of Azure Databricks, Microsoft Fabric, and Azure Synapse Analytics. Exercise: Create a comparative summary of when to use specific Azure data services versus Databricks-native solutions to guide customer decision-making.

Your preparation checklist

Map Databricks to Azure Services
Prepare a technical comparison or architectural diagram showing how Databricks integrates with Azure Data Lake Storage, Microsoft Fabric, and Azure AI Services to solve specific enterprise data challenges.
Develop a Well-Architected Framework Case Study
Draft a summary of a past project where you applied the Well-Architected Framework or Cloud Adoption Framework to optimize security, performance, or cost for a large-scale data platform.
Prepare a Technical Blocker Resolution Scenario
Document a specific instance where you identified and resolved a complex technical blocker during a production deployment, highlighting your troubleshooting process and communication with stakeholders.
Draft Thought Leadership on Generative AI
Prepare a brief perspective on how Generative AI workloads can be effectively deployed on Azure Databricks, focusing on governance, scalability, and business value realization.
Formulate Strategic Questions for the Interviewer
Prepare questions regarding the current challenges in the Databricks-Azure partner ecosystem, how the Architect's Office prioritizes technical blockers, and how cross-functional teams measure success for AI transformation projects.

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