They let you pick the project, which sounds like a gift but actually made it harder.
Choose a project where you played a central role and can clearly articulate the problem, your design decisions, trade-offs, and measurable impact. Structure your answer as a narrative: context, problem, constraints, your ownership, design choices with alternatives considered, trade-offs, results, and learnings. Keep it concise but detailed enough to demonstrate technical depth and ownership.
Pro tip: Quantify the outcome with metrics (e.g., latency reduction, cost savings, revenue impact) and explicitly state what you would do differently next time—this shows self-awareness and growth mindset, which interviewers at Ramp value.
Briefly describe the project's background, the business or technical problem, and why it mattered. Include constraints like timeline, team size, or legacy systems.
Clarify what you personally owned versus what the team did. Highlight specific responsibilities, decisions you drove, and how you collaborated with others.
Walk through the key design decisions, alternatives you considered, and the trade-offs (e.g., consistency vs. availability, build vs. buy, performance vs. simplicity). Explain why you chose your approach.
Summarize how you executed the design, any obstacles you overcame, and how you adapted. Mention technologies, tools, and processes used.
Quantify the results (e.g., metrics, impact) and reflect on what you learned or would do differently. Connect it to the role at Ramp.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements (scale, latency, accuracy, cost) and then propose a high-level architecture that separates ingestion, processing, storage, and serving. Walk through each component, emphasizing trade-offs (e.g., push vs pull, exact vs approximate counting, in-memory vs persistent storage) and justify choices based on Ramp's needs. Conclude by explaining how the system surfaces metrics to dashboards and alerts, ensuring real-time updates and scalability.
Pro tip: Demonstrate awareness of cost and operational complexity by discussing how you'd handle late data and out-of-order events, and propose a hybrid approach that balances accuracy and performance (e.g., using both streaming and batch for reconciliation).
Ask questions to understand scale (events per second), latency requirements (real-time vs near-real-time), accuracy needs (exact vs approximate), and budget. This shapes the entire design.
Propose a scalable ingestion layer (e.g., API gateway, message queue like Kafka or Kinesis) to handle high-throughput page view events. Discuss partitioning and ordering guarantees.
Explain rolling window semantics (e.g., sliding window of 1 minute) and how to compute counts. Compare approaches: exact counting with state stores vs approximate with sketches (e.g., HyperLogLog) or time-series databases.
Select a stream processing framework (e.g., Flink, Spark Streaming) and storage (e.g., Redis, Cassandra, time-series DB) that supports low-latency reads and writes. Discuss retention and aggregation strategies.
Describe how to expose metrics via APIs or push to monitoring systems (e.g., Prometheus, Grafana). Explain alerting logic (e.g., threshold-based) and how to handle real-time updates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.