SQL & Data Modeling

Writing non-trivial SQL queries Designing schemas for analytical workloads Normalization vs denormalization Partitioning, clustering, indexing

Coding & Algorithms

Implementing basic data transformations using Python or Java Manipulating arrays, strings, hash maps Focus on clean, efficient code — not LeetCode “hard” algorithms, but solid reasoning

Data Engineering Concepts

ETL pipeline design: source → transformation → sink Handling late-arriving data, deduplication, error handling Batch vs streaming tradeoffs Familiarity with GCP tools like Dataflow, Pub/Sub, BigQuery (if mentioned in resume)

Islands in a binary tree