LexisNexis·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say

Jun 2026

Summary

Interviewed for a TPM role at LexisNexis focused on large-scale content ingestion. The whole thing was basically one big system design problem and they went deep on it.

Questions Asked (1)

You're a TPM at a news company. The number of content sources has grown 10x. How would you redesign the ingestion pipeline to stay scalable, and what's a reasonable SLA for indexing over 5 billion documents?

System DesignTechnical Trade-offsAdaptability & Ambiguity

Author's notes

This one took me a minute to even scope properly.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a scalable, decoupled ingestion architecture using a distributed message queue and parallel processing. Address the SLA by breaking down the indexing pipeline into stages, defining latency targets for each, and justifying a reasonable end-to-end SLA based on business needs and technical feasibility.

Pro tip: Demonstrate awareness of trade-offs: for example, choosing between consistency and latency, or cost vs. throughput. Also, mention the importance of monitoring and backpressure to handle spikes gracefully.

1. Clarify Requirements and Constraints

Ask about the nature of content sources, expected growth rate, latency requirements, and existing infrastructure. Confirm the definition of 'indexing' and the SLA expectations.

2. Design a Scalable Ingestion Architecture

Propose a decoupled pipeline: sources -> message queue (e.g., Kafka) -> stream processing (e.g., Flink) -> storage/indexing. Ensure horizontal scalability, fault tolerance, and exactly-once semantics.

3. Address Indexing at Scale

Discuss partitioning, sharding, and parallel indexing. Consider using a distributed search engine like Elasticsearch with appropriate sharding and replication. Mention batch vs. near-real-time indexing.

4. Define a Reasonable SLA

Break down the pipeline into stages and assign latency budgets. For 5B documents, propose an SLA like '95% of documents indexed within 15 minutes of ingestion' and justify with throughput calculations.

5. Discuss Trade-offs and Monitoring

Highlight trade-offs between latency, cost, and consistency. Emphasize the need for monitoring, alerting, and backpressure to maintain SLA under load.

Key Points to Mention

Use of distributed message queues (e.g., Kafka) for decoupling and buffering
Stream processing frameworks (e.g., Flink, Spark Streaming) for scalable transformation
Partitioning and sharding strategies for indexing (e.g., by source or document ID)
Exactly-once processing semantics to avoid duplicates
Backpressure and autoscaling to handle variable load
SLA definition with percentiles (e.g., p95) and stage-wise latency budgets

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