Microsoft·Technical Product Manager·Onsite - System Design / Architecture·Senior

Senior

Jun 2026

Summary

Interviewed for a TPM role at Microsoft, got a question around data architecture. Not much else to go on from my notes but it was a technical conversation.

Questions Asked (1)

How would you approach designing a data architecture for a large-scale system?

System DesignData ModelingTechnical Trade-offs

AI HintsAI Generated

Suggested Approach

Start by clarifying the business goals, scale, and key use cases to ground your design in requirements. Then walk through a structured framework covering data sources, storage, processing, and serving layers, while highlighting trade-offs and Microsoft-specific technologies. Conclude by discussing how you would validate the architecture and evolve it over time.

Pro tip: Emphasize the importance of aligning data architecture with business outcomes and cost efficiency, and mention how you would leverage Azure services like Synapse, Databricks, and Cosmos DB to meet diverse needs. Show that you consider non-functional requirements like security, compliance, and latency from the start.

1. Clarify Requirements and Constraints

Ask questions to understand data volume, velocity, variety, access patterns, latency, consistency, and budget. Identify key stakeholders and business objectives.

2. Define Data Model and Storage Strategy

Choose appropriate data models (relational, document, graph, etc.) and storage solutions (SQL, NoSQL, data lake) based on access patterns and consistency needs. Consider polyglot persistence.

3. Design Data Processing and Integration

Determine batch vs. stream processing, ETL/ELT pipelines, and data integration patterns. Select technologies like Azure Data Factory, Synapse, or Databricks.

4. Plan Data Serving and Consumption

Design how data will be served to applications, analytics, and ML models. Consider APIs, data marts, caching, and real-time serving layers.

5. Address Non-Functional Requirements and Governance

Incorporate security, compliance, monitoring, scalability, and cost optimization. Define data governance, lineage, and quality processes.

Key Points to Mention

Scalability and elasticity to handle growth
Data consistency models (ACID vs. BASE) and trade-offs
Latency requirements and real-time vs. batch processing
Cost optimization and total cost of ownership
Security, privacy, and compliance (e.g., GDPR, HIPAA)
Leveraging Microsoft Azure services (e.g., Azure Synapse, Cosmos DB, Data Lake)

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.