Qumulo India Data Engineer Interview: Rounds, Questions and Preparation

What the job involves

Data Engineers build and maintain the pipelines that move and transform data from source systems into storage and analytics platforms. At fast-growing global tech companies like Qumulo India, they work on distributed systems, cloud storage, and large-scale data processing, often handling petabytes of file and object data. Day to day, they write code, debug pipelines, optimise queries, and collaborate with product and analytics teams.

Role and technical knowledge

1. 1

   What is the difference between ETL and ELT, and when would you choose one over the other?

   Explain that ETL transforms before loading while ELT loads raw data and transforms later. Mention that ELT is common with cloud data warehouses due to scalable compute.

2. 2

   How does a distributed file system like Qumulo's handle large files across multiple nodes?

   Cover data partitioning, replication for fault tolerance, metadata management, and how clients access data without knowing physical location.

3. 3

   Explain how you would design a schema for a fact table and dimension tables in a data warehouse.

   Describe star schema, surrogate keys, slowly changing dimensions, and how it supports analytical queries. Mention trade-offs with snowflake schema.

4. 4

   What are the key differences between object storage and block storage, and where does each fit in a data pipeline?

   Object storage is for unstructured data at scale with HTTP access, block storage is for low-latency random I/O. Pipelines often land raw data in object storage then process it.

Practical scenarios and problem solving

1. 5

   A daily data pipeline is running slower each day as data volume grows. How would you diagnose and fix it?

   Check for full table scans, missing indexes, skewed partitions, or inefficient joins. Suggest incremental processing, partitioning, and query tuning.

2. 6

   You need to ingest millions of small files from a source into a data lake. What challenges do you anticipate and how would you handle them?

   Mention small file problem, metadata overhead, and the need to batch or compact files. Suggest using a compaction job or a format like Parquet.

3. 7

   How would you ensure data quality and handle bad records in a production pipeline?

   Describe validation checks, dead-letter queues, logging, and alerting. Mention idempotency and reprocessing strategies.

4. 8

   If a pipeline fails midway, how do you make it restartable without duplicating data?

   Explain checkpoints, idempotent writes, and transactional guarantees. Mention using unique keys or upserts to avoid duplicates.

Coding and SQL

1. 9

   Write a SQL query to find the second highest salary from an employee table.

   Use a subquery with MAX or a window function like DENSE_RANK. Explain handling of ties and nulls.

2. 10

   Given a list of integers, write a function to find all pairs that sum to a target value.

   Use a hash set for O(n) time. Discuss edge cases like duplicates and no solution.

3. 11

   How would you find duplicate records in a table using SQL?

   Use GROUP BY with HAVING COUNT(*) > 1. Mention using window functions like ROW_NUMBER for more detail.

4. 12

   Write a Python function to read a large CSV file and compute the average of a numeric column without loading it all into memory.

   Use chunked reading with pandas or csv module. Discuss memory efficiency and handling missing values.

Behavioural and HR

1. 13

   Tell me about a time you had to learn a new technology quickly for a project.

   Use STAR method. Focus on the learning process, resources used, and the outcome. Show curiosity and self-drive.

2. 14

   Describe a situation where you had to work with a team to solve a complex problem.

   Highlight collaboration, communication, and your specific contribution. Mention how you handled disagreements.

3. 15

   Why do you want to work as a Data Engineer at a fast-growing storage company like Qumulo?

   Connect your interest in large-scale data, distributed systems, and cloud storage. Show you understand the company's domain.

4. 16

   How do you prioritise tasks when you have multiple deadlines?

   Discuss using urgency and impact, communicating with stakeholders, and breaking down tasks. Give a concrete example.