Reddit Data Engineer Interview Guide

Most candidates prep for Reddit's data engineer loop like it's a standard big-tech pipeline role. The data here is fundamentally social graph data, though, with deeply nested comment trees, vote signals flowing in real time, and a massive number of active communities each generating their own behavioral patterns. Candidates who practice flat event-stream designs tend to freeze when asked to model recursive, graph-like structures at warehouse scale.

Reddit Data Engineer Role

You're building the data backbone for a platform where every upvote, comment thread, and ad impression feeds downstream systems that product, ads, and trust & safety teams bet their roadmaps on. Success after year one means you own a critical slice of that ecosystem (the ad event attribution flow, the subreddit engagement metrics layer, or the content safety data feeds), your tables have clean SLAs that downstream teams actually trust, and you've shipped at least one project that measurably improved data freshness or cost efficiency. The bar is pipeline ownership that multiple teams depend on, not just pipelines that run green.

A Typical Week

What catches people off guard is how much infrastructure and ops work shows up relative to pure coding. You might spend a morning triaging a broken user_sessions table because a mobile client shipped malformed session-end events, then pivot to testing a Spark repartitioning job on a staging cluster that afternoon. If you hate ops work and want pure feature development, this isn't your role.

Projects & Impact Areas

Ad measurement pipelines sit at the center of Reddit's revenue, connecting subreddit engagement signals to advertiser conversion events through pre-aggregated CTR tables that the ads DS team iterates on weekly. Equally important but less visible: content safety data flows that feed ML models detecting spam and brigading across Reddit's communities, plus the schema evolution and anomaly detection work that keeps those pipelines trustworthy when upstream Kafka topics change without warning.

Skills & What's Expected

Production software engineering chops are underrated for this role. Reddit expects you to review a teammate's Kafka consumer for idempotency guarantees and reason about Avro schema registration, not just write SQL transformations. ML knowledge matters at a supporting level (the skill expectation is medium, not zero), since you'll build feature pipelines and training datasets, but the expert-level bar is squarely on data architecture: partitioning strategies that target major scan-size reductions in Trino, streaming semantics for vote event ingestion, and orchestration patterns in Airflow.

Levels & Career Growth

L5 Senior is a common entry point for external hires. The jump to L6 Staff is where careers stall, and the blocker is almost always scope rather than skill. Staff requires demonstrated cross-team platform impact: designing Reddit's shared metrics layer, leading a migration to a new table format like Iceberg, or setting data quality standards that multiple pods adopt.

Work Culture

Reddit calls its policy "Reddit is where you are," meaning remote-first with optional SF HQ access for in-person collaboration weeks. On-call rotations are taken seriously and deep work blocks are protected, but post-IPO growth pressure creates real tension: competing priorities from ads, trust & safety, and product analytics teams mean you'll context-switch more than at a pure infrastructure shop. Cross-functional collaboration with ML engineers, product analysts, and safety teams is constant, which is energizing if you like variety.

Reddit Data Engineer Compensation

The equity component matters more than base here. Looking at the level data, stock grants scale steeply from L5 to L6 and beyond, which means your real compensation trajectory at Reddit is tied to RSU performance over multiple years. The initial RSU grant is your strongest negotiation lever, because the data shows wide total comp ranges within each level (L5 spans $220K to $360K, for example), and the offer negotiation notes confirm equity is the most flexible component. Sign-on bonuses are also explicitly on the table if you're walking away from unvested equity elsewhere, but you'll need to raise it yourself.

When evaluating an offer, pay close attention to how much of your total comp sits in stock versus cash. At L6 Staff, RSUs represent nearly half the package. Reddit is a post-IPO public company, so those grants carry real market risk that a pure base-salary bump wouldn't. If you're weighing Reddit against another offer, map out the quarterly vesting schedule and think about what your year-two and year-three comp looks like under different stock scenarios, not just the headline number on the offer letter.

Reddit Data Engineer Interview Process

Plan for about four weeks from your first recruiter call to a final offer. The hiring committee, not individual interviewers, makes the go/no-go decision, so one great round won't single-handedly save you if another round falls flat.

Unclear data modeling grain is the rejection reason that shows up most often in the common feedback patterns. Candidates who can't articulate primary keys, join cardinalities, and fact table grain during the SQL & Data Modeling round get flagged as risky hires for a platform where stale or duplicated metrics directly affect Reddit's ad auction revenue and feed ranking quality. Operational ownership signals matter across every technical round too: if your System Design omits monitoring, alerting, and backfill strategy, or your coding solution ignores late-arriving events, those gaps reinforce a "not production-ready" narrative that's tough to overcome in committee.

Reddit Data Engineer Interview Questions

1WITH dedup AS (
2  SELECT
3    user_id,
4    post_id,
5    event_id,
6    event_ts,
7    ROW_NUMBER() OVER (
8      PARTITION BY event_id
9      ORDER BY event_ts ASC
10    ) AS rn
11  FROM event_log
12  WHERE event_name = 'upvote'
13    AND event_id IS NOT NULL
14    AND user_id IS NOT NULL
15    AND post_id IS NOT NULL
16)
17SELECT
18  DATE_TRUNC('day', event_ts) AS event_day,
19  user_id,
20  COUNT(*) AS upvote_events_deduped,
21  COUNT(DISTINCT post_id) AS unique_posts_upvoted
22FROM dedup
23WHERE rn = 1
24GROUP BY 1, 2
25ORDER BY 1, 2;
26

1WITH imp AS (
2  SELECT
3    user_id,
4    ad_id,
5    impression_ts,
6    DATE_TRUNC('day', impression_ts) AS impression_day
7  FROM ad_impressions
8  WHERE impression_ts >= DATE_ADD('day', -30, CURRENT_TIMESTAMP)
9    AND user_id IS NOT NULL
10    AND ad_id IS NOT NULL
11), clk AS (
12  SELECT
13    user_id,
14    ad_id,
15    click_ts
16  FROM ad_clicks
17  WHERE click_ts >= DATE_ADD('day', -30, CURRENT_TIMESTAMP)
18    AND user_id IS NOT NULL
19    AND ad_id IS NOT NULL
20)
21SELECT
22  impression_day AS event_day,
23  COUNT(DISTINCT user_id) AS users_impressed_then_clicked_10m
24FROM (
25  SELECT DISTINCT
26    i.impression_day,
27    i.user_id
28  FROM imp i
29  JOIN clk c
30    ON c.user_id = i.user_id
31   AND c.ad_id = i.ad_id
32   AND c.click_ts >= i.impression_ts
33   AND c.click_ts < i.impression_ts + INTERVAL '10' MINUTE
34) x
35GROUP BY 1
36ORDER BY 1;
37

The distribution skews heavily toward design judgment over rote recall. Pipelines & Streaming compounds with Data Quality & Observability in a way that catches people off guard: a question about deduplicating at-least-once Kafka events into a Parquet lake isn't just a streaming question, because the interviewer will push you into how you'd detect and alert on duplicates leaking into downstream sessionization tables that power Reddit's feed engagement metrics. If you're prepping, resist the urge to treat each area as its own silo. The sample questions show Reddit interviewers chaining concerns across layers (ingestion correctness, schema design, incident triage on the same comment-tree or post-view data), so your practice should mirror that connective thinking.

Practice Reddit-style questions across all six areas at datainterview.com/questions.

How to Prepare for Reddit Data Engineer Interviews

Reddit's revenue reached $2.2B in 2025, up roughly 70% year-over-year, with advertising as the only reported business segment. Two bets define where data engineering effort is going: an AI-powered shopping search feature that merges community recommendations with e-commerce intent, and a broader push to improve the ad platform to attract a wider range of advertisers.

Both of those initiatives are pipeline problems at their core. Shopping search needs real-time signals from product mentions scattered across thousands of subreddits. The ad platform improvements require connecting subreddit engagement data to advertiser conversion events with low latency and high accuracy.

When you answer "why Reddit," skip the personal fandom angle and talk about a specific pipeline challenge. Reddit's nested comment trees create schema design headaches you won't encounter at a flat-feed social app, because every post spawns a recursive graph of replies, each carrying its own vote trajectory. Mention that, or talk about how Reddit's community structure means engagement signals are clustered by subreddit rather than by individual user profile, which changes how you'd partition and model data for ad targeting. Connecting Reddit's revenue model to concrete data engineering tradeoffs will land far better than enthusiasm alone.

Try a Real Interview Question

1WITH base AS (
2  SELECT
3    e.user_id,
4    CAST(e.event_ts AS DATE) AS event_date,
5    e.event_ts,
6    e.ingest_ts
7  FROM events e
8  WHERE e.ingest_ts <= CAST(e.event_ts AS DATE) + INTERVAL '1' DAY
9), eligible AS (
10  SELECT
11    b.user_id,
12    b.event_date
13  FROM base b
14  LEFT JOIN user_bans ub
15    ON ub.user_id = b.user_id
16   AND ub.banned_ts <= b.event_date
17  WHERE ub.user_id IS NULL
18), dau_users AS (
19  SELECT DISTINCT
20    user_id,
21    event_date
22  FROM eligible
23)
24SELECT
25  event_date,
26  COUNT(*) AS dau
27FROM dau_users
28GROUP BY event_date
29ORDER BY event_date;

Reddit's coding rounds lean toward problems where you process large, semi-structured event data (vote streams, comment hierarchies) and make deliberate data structure choices, not just chase optimal Big-O on a textbook graph problem. The AI shopping search and ad platform work mean interviewers care whether you can handle late-arriving events and nested structures in code that's actually deployable. Build reps on similar problems at datainterview.com/coding.

Test Your Readiness

After this quiz, sharpen your weak spots at datainterview.com/questions, focusing on SQL debugging over Reddit-style schemas and system design prompts tied to ad engagement pipelines.