Perplexity — Data Engineer
Level: Mid-Level
Round: Phone Screen · Type: Technical Discussion · Difficulty: 6/10 · Duration: 60 min · Interviewer: Neutral
Topics: Data Lake Architecture, Large Language Models, Data Pipelines, Data Quality, Data Ingestion, Data Preprocessing, Tokenization, Data Lake Components, Stream Data Processing, Batch Processing, Data Access Patterns, Real-time Queries, Batch Processing, Interactive Analysis, Snowflake, Redshift
Location: San Francisco Bay Area
Interview date: 2025-03-01
Summary
I had a phone screen interview focused on designing a data lake architecture for training large language models.
Details
The interview started with a question about how to design a data lake architecture for training large language models (LLM). The goal was to create a scalable and efficient data pipeline capable of handling massive amounts of text data from various sources.
I was asked about:
- The types of data I would collect to train an LLM, and how to ensure data quality and diversity. I mentioned the Nvidia framework (https://arxiv.org/pdf/2412.02595).
- Potential problems I might encounter when ingesting data from the internet or diverse datasets.
- How I would handle data preprocessing, such as tokenization and data merging.
- Details about the components used to build the data lake, including data ingestion tools, storage solutions, data processing frameworks, and data catalog systems. We spent a long time discussing this question and sketching out some ideas.
- How I would decide which parts of the data pipeline should be stream data processing and which should be batch processing jobs, and what trade-offs I would consider when making this decision.
- The types of data that would need more regular updates, and which data might be better suited to remain older.
- I mentioned using Snowflake or Redshift. I was asked why I would choose those and why not other options.
- The different data access patterns needed and why. For example, whether I needed to support real-time queries, batch processing, or interactive analysis, and how I would design the architecture to accommodate these different access patterns.