Fetch Rewards data engineer candidates describe a take-home assignment followed by a Zoom interview, and in two cases an onsite covering coding, SQL, and architecture. Here is what they report and how to prepare.
$134K
Avg. Base Comp
$158K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Candidates who describe the Fetch Rewards data engineer interview all start with a take-home assignment, and its size varies. One data engineer reports a fairly involved pipeline-building task. Another reports about a day spent cleaning a provided dataset. A third, who interviewed for a senior analytics engineer role, describes roughly 30 to 40 hours of work on a JSON dataset, a relational model, SQL, data quality checks and a short stakeholder message. Plan for a real block of time, and expect the work to come up again. One candidate was asked to walk through the take-home from start to finish, along with resume projects.
The live rounds put weight on SQL. One data engineer reports a 90-minute Zoom interview with a SQL challenge, writing queries from business questions and an entity relationship diagram. The analytics engineer reports a one-hour SQL test and a one-hour architecture review. That review covered a warehouse model from production data to a data mart, then S3 storage, partitioning and aggregations.
Coding came up in the onsite reports. One candidate was asked to build an LRU cache from scratch. Another found the problems manageable but was judged to have memorized one after a quiet two-minute setup, so narrate your reasoning from the start.
One data engineer says the role was posted as a mix of data engineering and DevOps, and technical questions on the infrastructure side were the weak spot. All three candidates report no offer, so this guide shows where people struggled, not what a successful loop looks like. Review pipelines, warehouse modeling, SQL and basic operations work.
Synthesized from 3 candidate reports by our editorial team.
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Synthesized from candidate reports. Individual experiences may vary.
Candidates report starting with a take-home. Data engineer candidates describe a pipeline-building task and a dataset-cleaning task that took about a day. An analytics engineer candidate describes a much larger assignment that took roughly 30 to 40 hours. Expect to explain your choices later.
Candidates report a Zoom interview after the take-home. One described going through resume projects and the take-home code step by step. Another described a 90-minute session with past experience questions and a SQL challenge built from business questions and an ERD. Technical questions near the end may probe DevOps topics.
One analytics engineer candidate reports talking with a couple of recruiters and hiring managers. That candidate then did a session with business analysts, writing pseudocode for data problems they described. Other candidates did not mention this stage, so treat it as possibly role-specific.
Two candidates describe onsite interviews, one lasting four hours and one lasting all day. One candidate was asked to implement an LRU cache from scratch. Another found the coding manageable but was read as having memorized a problem after a brief silent setup. Explaining your reasoning from the start may help.
The analytics engineer candidate reports a one-hour SQL test and a one-hour architecture review. The review covered a warehouse model from production data to a data mart, followed by S3 storage, partitioning and advanced aggregations. All three candidates reported no offer.
