Just finished my Reddit DE loop last month, L4 level, SF-remote hybrid role. Wanted to write this up while it's fresh because I didn't find much specific to the DE track when I was prepping.
Process was: recruiter call, then a 45-min technical screen, then a virtual onsite with 4 rounds.
The technical screen was split. First half: SQL. Not trick SQL, but the kind that actually tests whether you understand window functions and aggregations in a real context. I got something like "given this events table, find the 7-day rolling average of DAU" and had to walk through it. Second half was a Python coding question about transforming nested JSON into a flat structure. Medium difficulty, nothing exotic.
Onsite breakdown: Round 1: SQL deep dive. Two questions, one was a funnel analysis (very Reddit-relevant given ads data), the other was about deduplication with CTEs. They use Spark + Redshift in prod so the interviewer mentioned they care whether you know the difference between distributed query patterns and single-node SQL. Round 2: System design for data. Design a pipeline that ingests Reddit post metadata at scale and makes it available for ML feature generation within 30 minutes. Classic Lambda/Kappa architecture territory. They pushed back when I hand-waved Kafka, wanted specifics on partitioning strategy and schema evolution. Round 3: Behavioral. Standard stuff but with a Reddit lens, "tell me about a time you dealt with upstream data quality issues" came up. They seem to care a lot about cross-functional collaboration with the ML platform team. Round 4: A second coding/pipeline round. Given a messy CSV of user events, write a Python pipeline to clean, transform, and output aggregated metrics. Focus was on correctness and then they asked how you'd scale it.
Comp for my offer was: base $165k, equity around $60k/yr vesting, standard benefits. This was for L4 in the Bay Area, early 2026. I ended up not taking it (went with another offer), but the process itself was solid. Interviewers were prepared, no runaround.
One thing I'll say: they really do care about data quality tooling. I mentioned having used Great Expectations and dbt tests and the interviewer visibly lit up. If you're coming from a pure ETL background without quality layers, brush up on that.