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This is a big open-ended question and I spent probably too long on the metrics enumeration part before getting to the architecture.
Start by clarifying requirements and scale, then walk through the end-to-end pipeline: metrics definition, ingestion, storage/aggregation, and serving. Emphasize trade-offs between real-time and batch processing, and how to optimize for dashboard query performance.
Pro tip: Anchor your design around the most critical user-facing metrics (e.g., DAU, latency, token usage) and show how each component directly supports those, rather than listing generic technologies.
Ask about expected data volume, query latency SLAs, and key stakeholders to scope the design appropriately.
Identify core product metrics (e.g., active users, conversation length, response time, error rates) and design a schema that supports them.
Choose between batch (e.g., Kafka + Spark) and stream (e.g., Kafka + Flink) processing based on freshness needs, and handle data quality and backpressure.
Select storage layers (e.g., data lake for raw, OLAP for aggregates) and pre-aggregate common dimensions to speed up queries.
Expose APIs for dashboards, use caching, materialized views, and query pushdown to meet latency SLAs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with a streaming approach, Kafka-style event bus pulling from the various service databases, then a consumer writing to the raw log and a separate aggregation job feeding the OLAP layer.
Start by clarifying requirements: data volume, latency, schema evolution, and query patterns. Then propose a scalable, fault-tolerant pipeline using a message queue (e.g., Kafka) to decouple producers from consumers, with stream processing for transformations and a columnar store (e.g., Snowflake, BigQuery) for analytics. Emphasize data modeling, exactly-once semantics, and monitoring.
Pro tip: Show awareness of Salesforce's multi-tenant architecture and data isolation requirements; mention how you'd handle per-tenant partitioning and ensure compliance with data residency and security policies.
Ask about data volume, velocity, variety, latency needs, and query patterns to tailor the design. Confirm non-functional requirements like fault tolerance, scalability, and security.
Propose a scalable ingestion layer using a distributed message queue (e.g., Kafka) to handle high-throughput events from chat sessions, message events, and user accounts. Ensure idempotent producers and schema registry for data contracts.
Use stream processing (e.g., Kafka Streams, Flink) to clean, enrich, and aggregate data in real-time or micro-batches. Handle late data, deduplication, and exactly-once semantics.
Load processed data into a columnar analytics store (e.g., Snowflake, BigQuery) optimized for analytical queries. Consider partitioning, clustering, and indexing for performance.
Implement monitoring, alerting, and dead-letter queues for failures. Use infrastructure-as-code for reproducibility and automate schema evolution and data quality checks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Dimensions were pretty intuitive: time, user cohort, geography, model version, session type.
Start by framing the dashboard's purpose and primary user personas to justify the slicing dimensions. Then discuss the trade-off between query freshness and cost by proposing a tiered architecture (e.g., real-time vs. batch) with clear SLAs. Conclude with how you would monitor and optimize the balance over time.
Pro tip: Tie freshness SLAs to business impact—not all data needs real-time; often near-real-time (e.g., 15-min) is sufficient and saves significant cost. Also, mention pre-aggregation and caching as levers to reduce compute while maintaining acceptable freshness.
Ask about the dashboard's goals and primary users to determine which slicing dimensions are most valuable. This ensures you prioritize dimensions that drive decisions.
List dimensions like time, geography, product, customer segment, and channel, explaining why each matters. Mention that dimensions should be chosen based on query patterns and cardinality.
Describe how real-time processing increases cost and complexity, while batch processing reduces cost but adds latency. Propose a tiered approach with different freshness levels for different data.
Detail techniques like pre-aggregation, materialized views, caching, and incremental processing to balance freshness and cost. Mention partitioning and indexing for efficient slicing.
Explain how you would monitor query performance, cost, and freshness SLAs, and iterate based on feedback. Highlight the importance of aligning with stakeholders on acceptable trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the question I was least prepared for.
Start by clarifying the data types and regulatory requirements, then propose a layered governance architecture that includes data classification, access control, encryption, and auditing. Emphasize a shift-left approach to embed privacy controls into the pipeline design, and discuss trade-offs between data utility and privacy.
Pro tip: Demonstrate familiarity with Salesforce's specific data governance tools and compliance standards (e.g., Shield, GDPR, CCPA) to show you understand the company's ecosystem. Also, highlight the importance of data minimization and purpose limitation as core principles.
Determine what data constitutes PII and classify it based on sensitivity and regulatory requirements. Use automated discovery tools to scan and tag PII across the pipeline.
Enforce least privilege access using role-based or attribute-based controls. Encrypt data at rest and in transit, and consider tokenization or pseudonymization for analytics.
Map controls to regulations like GDPR, CCPA, and HIPAA. Implement audit logging and monitoring to track data access and detect anomalies.
Collect only necessary data and define retention policies to purge data when no longer needed. Use aggregation or anonymization to reduce risk in analytics.
Discuss how governance impacts performance and cost, and propose solutions like column-level encryption or differential privacy. Ensure the design scales with data volume.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.