Data platform engineer technical fundamentals interview questions

As asked

A streaming pipeline aggregates real-time sales totals and serves them on a dashboard. A business user asks: is the number on the dashboard exactly correct right now? How do you explain the consistency guarantees and what engineering choices determine how stale the number can be?

Sample answer outline

The number is accurate as of the latest processed watermark, not wall clock time. Staleness is bounded by the watermark lag (typically the out-of-orderness allowance plus processing lag) plus the sink write interval. The candidate should explain the difference between event time and processing time aggregations, and how choosing processing time gives a smaller latency bound at the cost of missing late events. They should be able to give a realistic expected staleness for a 5-minute Flink aggregation job.

Expect these follow-ups

• What would you change if the business required the number to always reflect events up to exactly 1 minute ago?
• How does this differ in a micro-batch Spark Structured Streaming job?

As asked

How does the CAP theorem apply to a lakehouse built on object storage? When you have concurrent readers and writers on an Iceberg table, what guarantees do you actually get, and what trade-offs does the table format's atomic commit mechanism represent?

Sample answer outline

Object storage is not a distributed database in the CAP sense, but the lakehouse table format creates an eventually consistent system where concurrent readers see the latest committed snapshot and writers use optimistic concurrency with atomic catalog swaps. Iceberg provides isolation at snapshot granularity: a reader always sees a consistent point-in-time view, but a writer that conflicts on the same files must retry. The tradeoff is availability (writers can succeed eventually) over strict linearizability. The candidate should note that the catalog backend (Hive Metastore, Nessie, REST) is the actual consistency boundary.

Expect these follow-ups

• How does using Nessie as a catalog change the consistency model?
• What happens if the catalog's atomic swap fails partway through?

As asked

Explain how an analytical query engine like Trino or Spark uses Parquet column statistics to skip files and row groups. What types of predicates benefit from this and which do not?

Sample answer outline

Each Parquet file stores column-level min/max statistics per row group in the footer. The query engine reads the footer before reading any data and skips row groups where the predicate cannot match based on the min/max range. Range predicates (ts > '2024-01-01') and equality predicates on sorted or low-cardinality columns benefit most. Predicates on OR conditions, NOT, and high-cardinality string columns (LIKE with a leading wildcard) cannot use statistics. Bloom filters complement statistics for equality predicates on high-cardinality columns.

Expect these follow-ups

• How does column ordering within a row group affect the effectiveness of statistics?
• What is the cost of reading Parquet footers for a table with 10 million small files?

As asked

Kafka's default partitioner uses a murmur2 hash of the message key to assign partitions. Explain how this works, what causes partition skew, and what alternatives exist when your key space is not evenly distributed.

Sample answer outline

The default partitioner applies a murmur2 hash modulo the number of partitions to the key bytes. This is a simple modulo hash, not consistent hashing (which is a ring-based technique used to minimize key redistribution when cluster topology changes). Skew occurs when a small number of keys are extremely frequent (a single high-volume user) or when the key space is small relative to partition count. Alternatives include salting the key with a random suffix and using a separate lookup to reassemble, using a custom partitioner that routes hot keys to a dedicated partition, or using sticky partitioning for null-key messages to reduce small batch overhead.

Expect these follow-ups

• How does repartitioning a Kafka topic (changing partition count) affect existing consumers?
• What is the difference between murmur2 and murmur2_random partitioners?

As asked

RocksDB uses an LSM tree and Parquet is a columnar format. For a write-heavy streaming use case, what are the fundamental read and write amplification tradeoffs of each, and how does this inform your choice of state store backend in Flink?

Sample answer outline

LSM trees buffer writes in a sorted in-memory structure and flush to immutable SSTables, resulting in low write amplification but higher read amplification because reads must merge multiple levels. Parquet is optimized for reads: columnar layout allows vectorized scans but requires full file rewrites for updates. For Flink state (key lookups and point updates) RocksDB's LSM is a natural fit because state operations are key-value lookups and sequential writes, not full column scans. Parquet would be unsuitable as a Flink state store because random key lookups on a columnar file are expensive.

Expect these follow-ups

• How does RocksDB compaction affect Flink checkpoint times?
• What is the tradeoff between RocksDB BLOCK_CACHE size and memory available for Flink network buffers?