Big Data Engineer Interview Questions UK: 8 Real Questions and What Interviewers Want

Applying for a Big Data Engineer job in the UK? Here is what you are likely to be asked. These questions come from our Big Data Engineer question bank of 12 questions, written for UK employers at mid-level level.

Eight Big Data Engineer interview questions, and what the interviewer is thinking

1. What is the difference between RDDs, DataFrames and Datasets in Spark, and why do DataFrames usually perform better?

Why they ask: Spark is central to most big data roles, and this shows whether you understand what happens under the hood.

2. Explain how Kafka works. What are topics, partitions, consumer groups and offsets, and how do you get reliable delivery?

Why they ask: Streaming ingestion with Kafka is a staple of modern big data platforms.

3. What does a typical day as a Big Data Engineer involve, and what do you enjoy about it?

Why they ask: They want to check your expectations of the role match reality.

4. How do partitioning and bucketing work in Hive, and what is the difference between managed and external tables?

Why they ask: Hive and its metastore remain common in Hadoop-based estates, and table design affects performance and safety.

5. Tell me about a time you onboarded a large-volume data source onto a Hadoop or Spark platform.

Why they ask: They want practical experience of designing and delivering ingestion at scale.

6. A critical overnight Spark job has failed with out-of-memory errors for the first time. What do you do?

Why they ask: Production troubleshooting under time pressure is a daily reality in this role.

7. What is the small files problem in HDFS and cloud object storage, and how would you address it?

Why they ask: It is a frequent real-world cause of poor performance and tests practical platform experience.

8. Consumer lag on a critical Kafka topic is growing steadily and downstream dashboards are falling behind. How do you investigate?

Why they ask: It tests your ability to diagnose streaming systems and tell a capacity issue from a bug.

A sample answer to question 1

What is the difference between RDDs, DataFrames and Datasets in Spark, and why do DataFrames usually perform better?

RDDs are Spark's low-level distributed collections. They give full control through functional transformations, but Spark can't see inside my lambda functions, so it can't optimise them. DataFrames add a schema and named columns, and Datasets, available in Scala and Java, add compile-time type safety on top. The performance gain comes from the Catalyst optimiser and the Tungsten execution engine: because Spark understands the logical plan, it can push filters down, prune unused columns, reorder joins and generate efficient code with off-heap memory. Transformations are lazy, so nothing runs until an action such as write or count triggers the plan. In practice I use DataFrames or Spark SQL for nearly everything, with built-in functions rather than Python UDFs, which serialise rows between the JVM and Python and are slow. I only drop to RDDs for unusual custom processing, and I check explain() output to confirm filters are pushed down and partitions are pruned as expected.

Common mistake to avoid: Treating RDDs as the default; Ignoring query plans

Want the rest?

Full model answers, key points and common mistakes for all 12 questions are on the Big Data Engineer interview questions page. The first five are free, no sign-up needed.