Real, anonymous reports from people who interviewed for Data Engineer at Google, newest first and distilled into what to expect across the loop.
Interview process included a recruiter call, a screening, and three technical rounds covering SQL/basic data engineering and DSA, followed by system design, a structured bar-raiser, team matching, and final discussion with a data engineering leader.
Technical interview focused on writing non-trivial SQL, data modeling and schema design tradeoffs (normalization vs denormalization, partitioning/indexing), plus implementing straightforward data transformations in Python/Java and discussing ETL (batch vs streaming, late data, deduplication, error handling) with relevant GCP tools.
The interview process typically includes a recruiter call, a technical screening, followed by three technical rounds, a system design discussion, a bar-raiser interview, team matching, and a final conversation with a data engineering leader.
Candidates should prepare for technical interviews that focus on SQL, data modeling, and schema design, as well as implementing data transformations in languages like Python or Java.
Expect to discuss ETL processes, including batch vs streaming data, deduplication, error handling, and the use of relevant GCP tools during technical interviews.
Candidates will likely participate in live coding sessions where they must explain their thought process while solving problems on a shared document, emphasizing clear communication.
Behavioral interviews will focus on collaboration and project experiences, so candidates should be ready to discuss their past work and teamwork dynamics.
Candidates expressed a desire to have practiced more on system design topics and to have reviewed common data engineering challenges prior to the interviews.