Anthropic Data Engineer Interview Guide

Anthropic's data engineering role trips up candidates who prep like it's a standard analytics or BI position. One pattern we see with candidates is underestimating how deeply this job is wired into the ML training and safety evaluation loop. You're not building dashboards for stakeholders. You're building the pipelines that determine whether Claude's next iteration is safe to ship.

Anthropic Data Engineer Role

At Anthropic, a data engineer owns the infrastructure that feeds Claude's training, evaluation, and product analytics systems end to end. That means building orchestrated pipelines that move raw conversation logs and human preference annotations into clean, versioned datasets where the RLHF and Constitutional AI teams consume them. Success after year one looks like this: the safety evals team can reproduce any benchmark run against a pinned data snapshot you built, and the training team trusts your pipelines enough to kick off a new Claude iteration without manually spot-checking upstream data.

A Typical Week

The split that catches people off guard is how little of the week is pure coding. Infrastructure work and written artifacts (design docs, RFCs, runbooks) eat a surprisingly large share, because when your pipelines feed safety-critical model evaluations, tribal knowledge becomes a liability. On-call is real and rotational, not theoretical, and your Monday morning starts by reviewing whether weekend pipeline runs left any partition gaps that could block the RLHF team.

Projects & Impact Areas

The flagship work is the LLM evaluation data lifecycle: pipelines that capture Claude's outputs, normalize scorer results, and land partitioned tables the alignment science team uses for harmlessness benchmarks. That work bleeds into RLHF training infrastructure, where schema changes (like adding a new reward signal column) force you to negotiate data contracts with the model training team and handle backfills without breaking existing runs. On a completely different axis, Claude's API now serves millions of users and enterprise customers, so usage telemetry, billing data flows, and go-to-market analytics all need the same pipeline rigor you'd apply to training data.

Skills & What's Expected

The overrated prep area is visualization and dashboarding, which barely registers in day-to-day work. The underrated one is understanding how LLM training and inference pipelines actually work, because your cross-functional syncs aren't with product managers asking for metrics. They're with ML researchers and safety teams who need you to reason about schema evolution in the context of RLHF reward signals and Constitutional AI feedback loops. What separates strong candidates is the ability to explain why a broken dedup step in the annotation pipeline is an AI safety problem, not just a data quality inconvenience.

Levels & Career Growth

Most external hires land at ICT3 (Mid) or ICT4 (Senior), with ICT2 reserved for candidates under two years of experience and ICT5 Staff being exceptionally rare from outside. Promotion from Senior to Staff at Anthropic hinges on demonstrating impact beyond your own team: leading cross-functional data platform initiatives that the RLHF and safety evals teams both depend on, and mentoring engineers in ways that visibly raise the bar. A Senior who improves the reliability of evaluation pipelines has a much clearer Staff trajectory than one who ships ten new data products.

Work Culture

Most data engineers work from the SF office 4-5 days a week given tight collaboration loops with research and training teams, even though the stated expectation is at least 25% in-office. The safety mission isn't performative: you'll eat lunch with the policy team, read internal docs on model architecture changes that affect your schemas, and feel genuine accountability when a pipeline failure could delay a safety evaluation.

Anthropic Data Engineer Compensation

Anthropic's equity follows a 4-year vesting schedule with a 1-year cliff, meaning nothing hits your account until month 13. Because Anthropic is still private, the real-world value of that equity depends entirely on what liquidity options exist when your shares vest. Candidates should pressure their recruiter for specifics on how and when vested equity can actually be converted to cash, because that single detail changes the math on the entire offer.

On negotiation: from what candidates report, competing offers can create meaningful leverage, particularly on equity grant size and signing bonuses. If you're weighing an Anthropic offer against one from a public company, lean into that contrast. Rather than fixating on base salary (which tends to be less flexible), ask pointed questions about whether a larger equity allocation or additional guaranteed cash better fits your risk tolerance.

Anthropic Data Engineer Interview Process

Plan for a slow burn. From candidate reports, a quiet gap of two to four weeks can appear after your final onsite while Anthropic handles team matching internally. That silence doesn't necessarily mean rejection, but it does mean you should keep other processes warm rather than pausing your search.

The rejection pattern that shows up most often across candidate accounts is a lack of genuine AI safety alignment. Anthropic's behavioral round explicitly probes how you think about responsible data handling and the downstream consequences of pipeline failures on Claude's safety evaluations. Candidates who treat that round as a checkbox, recycling generic STAR stories about disagreements, tend to get cut even when their technical rounds are solid. Consistency matters too: from what candidates report, each interviewer writes up their assessment independently, so one great round won't easily paper over a weak showing elsewhere.

Anthropic Data Engineer Interview Questions

from __future__ import annotations

from heapq import heappush, heappop
from typing import Dict, Iterable, List, Tuple


def top_k_users_by_tokens(
    logs: Iterable[dict],
    k: int,
) -> List[str]:
    """Return top k user_ids by total tokens_in + tokens_out.

    Constraints:
      - Treat logs as a stream (single pass).
      - Use O(k) extra memory for the top-k structure.
      - Aggregation dict grows with unique users, which is unavoidable if exact.

    Tie-break:
      - Higher total tokens first.
      - If tied, smaller user_id first.
    """
    if k <= 0:
        return []

    totals: Dict[str, int] = {}
    for row in logs:
        # Defensive parsing, common failure point in interviews.
        uid = row.get("user_id")
        if uid is None:
            continue
        tin = int(row.get("tokens_in", 0) or 0)
        tout = int(row.get("tokens_out", 0) or 0)
        totals[uid] = totals.get(uid, 0) + tin + tout

    # Maintain a min-heap of the current top-k.
    # Heap item: (total_tokens, negative user_id ordering is tricky for strings).
    # Instead, push (total_tokens, user_id) and pop the smallest, but we want to
    # keep larger totals, and for ties we want smaller user_id to rank higher.
    # So the "worst" item is smaller total, or same total with larger user_id.
    heap: List[Tuple[int, str]] = []

    for uid, total in totals.items():
        item = (total, uid)
        if len(heap) < k:
            heappush(heap, item)
        else:
            worst_total, worst_uid = heap[0]
            # If item is better than worst, replace.
            if (total > worst_total) or (total == worst_total and uid < worst_uid):
                heappop(heap)
                heappush(heap, item)

    # heap currently holds k best, but unordered. Sort to final ranking.
    heap.sort(key=lambda x: (-x[0], x[1]))
    return [uid for _, uid in heap]


if __name__ == "__main__":
    sample = [
        {"request_id": "r1", "user_id": "b", "ts": 1, "tokens_in": 5, "tokens_out": 5},
        {"request_id": "r2", "user_id": "a", "ts": 2, "tokens_in": 7, "tokens_out": 1},
        {"request_id": "r3", "user_id": "b", "ts": 3, "tokens_in": 0, "tokens_out": 1},
        {"request_id": "r4", "user_id": "c", "ts": 4, "tokens_in": 6, "tokens_out": 2},
    ]
    assert top_k_users_by_tokens(sample, 2) == ["b", "a"]

-- Assumes BigQuery Standard SQL
-- Tables:
--   raw_events(event_id, org_id, conversation_id, user_id, event_type, event_ts, ingested_at)
-- event_type examples: 'user_message', 'assistant_message', 'system'

WITH deduped_events AS (
  SELECT
    event_id,
    org_id,
    conversation_id,
    user_id,
    event_type,
    event_ts,
    ingested_at
  FROM (
    SELECT
      re.*,
      ROW_NUMBER() OVER (
        PARTITION BY event_id
        ORDER BY ingested_at DESC
      ) AS rn
    FROM raw_events re
  )
  WHERE rn = 1
),

-- Daily aggregation of conversations and message counts
user_day_metrics AS (
  SELECT
    org_id,
    user_id,
    DATE(event_ts) AS event_date,
    COUNT(DISTINCT conversation_id) AS daily_distinct_conversations,
    COUNTIF(event_type = 'user_message') AS daily_user_messages
  FROM deduped_events
  GROUP BY 1, 2, 3
),

-- Daily presence of a conversation for rolling distinct counts
user_day_conversation_presence AS (
  SELECT DISTINCT
    org_id,
    user_id,
    DATE(event_ts) AS event_date,
    conversation_id
  FROM deduped_events
),

rolling_7d_distinct_conversations AS (
  SELECT
    org_id,
    user_id,
    event_date,
    COUNT(DISTINCT conversation_id) AS rolling_7d_distinct_conversations
  FROM user_day_conversation_presence
  -- Count distinct conversations in the inclusive 7-day window ending on event_date
  GROUP BY 1, 2, 3
),

-- BigQuery cannot do COUNT(DISTINCT ...) as a window function reliably in all cases,
-- so do the rolling window with a self-join on the presence table.
rolling_7d AS (
  SELECT
    a.org_id,
    a.user_id,
    a.event_date,
    COUNT(DISTINCT b.conversation_id) AS rolling_7d_distinct_conversations
  FROM (
    SELECT DISTINCT org_id, user_id, event_date
    FROM user_day_conversation_presence
  ) a
  JOIN user_day_conversation_presence b
    ON b.org_id = a.org_id
   AND b.user_id = a.user_id
   AND b.event_date BETWEEN DATE_SUB(a.event_date, INTERVAL 6 DAY) AND a.event_date
  GROUP BY 1, 2, 3
)

SELECT
  udm.org_id,
  udm.user_id,
  udm.event_date,
  udm.daily_distinct_conversations,
  udm.daily_user_messages,
  COALESCE(r7.rolling_7d_distinct_conversations, 0) AS rolling_7d_distinct_conversations
FROM user_day_metrics udm
LEFT JOIN rolling_7d r7
  ON r7.org_id = udm.org_id
 AND r7.user_id = udm.user_id
 AND r7.event_date = udm.event_date
ORDER BY udm.org_id, udm.user_id, udm.event_date;

The question mix skews heavily toward building and operating data platforms, not just querying them. Where this gets tricky is that Anthropic's system design questions assume you already think in terms of pipeline reliability (backfill strategies, idempotency, schema evolution), so weak fundamentals in one area will crater your performance in the other. Candidates who prep only for SQL and coding often underestimate how much of the loop requires you to reason about Claude-specific constraints: how evaluation datasets must be versioned for reproducibility, why training data deduplication has safety implications, and what it means to build pipelines where a silent 0.8% row drop could distort a refusal-policy metric.

Drill questions that mirror these Anthropic-specific scenarios at datainterview.com/questions.

How to Prepare for Anthropic Data Engineer Interviews

Anthropic's north star is becoming the market leader in enterprise AI and coding while staying ad-free and expanding access without compromising user trust. That dual mandate shapes everything a data engineer touches. The company reached $14B in ARR, up 8x year-over-year, and has raised its 2026 revenue forecast to $1.8B. Revenue at that trajectory means your pipelines serve two masters simultaneously: the product side (Claude API usage telemetry, billing, enterprise customer analytics) and the safety research side (evaluation datasets, RLHF feedback loops, Constitutional AI data flows).

The "why Anthropic" answer that actually lands connects your data engineering background to the specific tension between Anthropic's safety rigor and its commercial velocity. Don't just say you care about responsible AI. Instead, reference how Anthropic's own research team documented the ways AI is transforming their internal workflows, then describe a concrete moment from your career where you had to protect data correctness under real shipping pressure. That's the framing interviewers remember.

Try a Real Interview Question

| eval_runs |
|-------------------------------|
| run_id | model_id | started_at           |
|--------|----------|----------------------|
| r1     | m1       | 2026-02-20 10:00:00  |
| r2     | m1       | 2026-02-23 09:00:00  |
| r3     | m2       | 2026-02-22 12:00:00  |
| r4     | m2       | 2026-02-10 08:00:00  |

| eval_examples |
|------------------------------------------------------------------|
| dataset_slice | example_id | total_in_slice |
|--------------|------------|----------------|
| safety       | e1         | 3              |
| safety       | e2         | 3              |
| helpfulness  | e3         | 2              |
| helpfulness  | e4         | 2              |

| eval_results |
|----------------------------------------------------------------------------------|
| run_id | example_id | evaluated_at          | status |
|--------|------------|-----------------------|--------|
| r2     | e1         | 2026-02-23 09:10:00   | pass   |
| r2     | e2         | 2026-02-23 09:11:00   | fail   |
| r3     | e3         | 2026-02-22 12:05:00   | pass   |
| r3     | e4         | 2026-02-22 12:06:00   | pass   |

WITH recent_runs AS (
  SELECT
    run_id,
    model_id,
    started_at,
    ROW_NUMBER() OVER (PARTITION BY model_id ORDER BY started_at DESC, run_id DESC) AS rn
  FROM eval_runs
  WHERE started_at >= (CURRENT_TIMESTAMP - INTERVAL '7 days')
), latest_runs AS (
  SELECT run_id, model_id
  FROM recent_runs
  WHERE rn = 1
), per_slice_totals AS (
  SELECT
    dataset_slice,
    MAX(total_in_slice) AS total_examples
  FROM eval_examples
  GROUP BY dataset_slice
), per_slice_evaluated AS (
  SELECT
    lr.model_id,
    ee.dataset_slice,
    COUNT(DISTINCT er.example_id) AS evaluated_examples,
    SUM(CASE WHEN er.status = 'fail' THEN 1 ELSE 0 END) AS failures
  FROM latest_runs lr
  JOIN eval_results er
    ON er.run_id = lr.run_id
  JOIN eval_examples ee
    ON ee.example_id = er.example_id
  GROUP BY lr.model_id, ee.dataset_slice
)
SELECT
  p.model_id,
  t.dataset_slice,
  t.total_examples,
  COALESCE(p.evaluated_examples, 0) AS evaluated_examples,
  CAST(COALESCE(p.evaluated_examples, 0) AS DOUBLE PRECISION) / NULLIF(t.total_examples, 0) AS coverage,
  CAST(COALESCE(p.failures, 0) AS DOUBLE PRECISION) / NULLIF(p.evaluated_examples, 0) AS failure_rate
FROM (SELECT DISTINCT model_id FROM latest_runs) p_models
CROSS JOIN per_slice_totals t
LEFT JOIN per_slice_evaluated p
  ON p.model_id = p_models.model_id
 AND p.dataset_slice = t.dataset_slice
ORDER BY p_models.model_id, t.dataset_slice;

Anthropic's coding rounds reward clean, production-quality Python over clever tricks. Expect applied data processing problems: messy inputs, edge cases around malformed records, and code that reads like it belongs in a reviewed PR rather than a notebook. Practice at datainterview.com/coding with a focus on iterator patterns, hash map lookups, and string parsing.

Test Your Readiness

Use datainterview.com/questions to drill SQL, data modeling, and behavioral questions calibrated for data engineering roles at AI companies.