LinkedIn Data Engineer Interview: Rounds, Questions and Preparation

What the job involves

Data Engineers build and maintain the systems that collect, store, and process data at scale. At global tech companies, they design pipelines that move data from sources to warehouses and data lakes, ensuring reliability, quality, and performance for analytics and machine learning. Day to day involves writing code, managing cloud resources, and collaborating with data scientists and analysts.

Role and Technical Knowledge

1. 1

   Explain the difference between ETL and ELT, and when would you use each in a big data environment?

   Contrast transformation before vs. after loading, and discuss use cases like cloud data warehouses where ELT is preferred for scalability.

2. 2

   How does a distributed processing framework like Apache Spark work, and what are RDDs?

   Cover Spark's in-memory computation, RDD immutability and lineage, and how it differs from MapReduce.

3. 3

   What are the key considerations when designing a data warehouse schema for analytics?

   Discuss star vs. snowflake schemas, fact and dimension tables, and trade-offs between normalization and query performance.

4. 4

   Describe how you would handle slowly changing dimensions (SCD) in a data warehouse.

   Explain SCD types (especially Type 2 for history tracking), implementation using surrogate keys and timestamps, and impact on queries.

Practical Scenarios and Problem Solving

1. 5

   You need to build a pipeline that ingests real-time clickstream data and makes it available for analytics within 5 minutes. What technologies and architecture would you choose?

   Propose a streaming solution like Kafka for ingestion, a stream processor (e.g., Spark Streaming or Flink), and a sink like a data lake or warehouse; mention trade-offs.

2. 6

   A daily ETL job that usually takes 2 hours suddenly starts taking 6 hours. How would you troubleshoot and resolve this?

   Outline steps: check logs, monitor resource usage, identify data volume changes, review query plans, and optimize bottlenecks.

3. 7

   How would you ensure data quality and consistency in a pipeline that aggregates data from multiple sources?

   Mention validation checks, deduplication, schema enforcement, and monitoring/alerting for anomalies.

4. 8

   Given a large dataset, how would you optimize a slow-running SQL query?

   Discuss indexing, partitioning, avoiding SELECT *, using EXPLAIN plans, and rewriting subqueries as joins.

Coding and Algorithms

1. 9

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

   Use a subquery with MAX or window functions like DENSE_RANK; explain handling of ties and NULLs.

2. 10

   Given a list of integers, write a function to return the top K frequent elements.

   Use a hash map to count frequencies and a heap or bucket sort for efficiency; discuss time and space complexity.

3. 11

   How would you detect and remove duplicates from a large dataset using Python?

   Mention using pandas drop_duplicates or set-based approaches, and consider memory constraints for large data.

4. 12

   Implement a function to merge two sorted arrays into one sorted array.

   Use two pointers for O(n+m) time; discuss edge cases like empty arrays.

Behavioral and HR

1. 13

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

   Use STAR method: describe the situation, how you learned (courses, docs, hands-on), and the positive outcome.

2. 14

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

   Highlight collaboration, your specific contribution, and how you overcame challenges together.

3. 15

   Why do you want to work as a Data Engineer at a global tech company?

   Connect your interest in large-scale data systems with the company's data-driven culture and impact; show enthusiasm for the role.

4. 16

   How do you handle tight deadlines or pressure when working on multiple data pipelines?

   Discuss prioritization, time management, communication with stakeholders, and staying calm under pressure.