DeepSeek Data Engineer Interview Guide

From hundreds of mock interviews with data engineering candidates targeting AI labs, one pattern keeps showing up: people prep for generic pipeline design questions and get blindsided when the interviewer asks how a data quality regression in pre-training corpora would actually degrade model output. At DeepSeek, the data engineer role sits so close to model training that you can't fake that connection.

DeepSeek Data Engineer Role

You're building and owning the data infrastructure that feeds models like DeepSeek-V3 and R1. That means deduplication pipelines for web-crawl ingestion, versioned dataset delivery for training runs, and quality filtering heuristics you'll iterate on directly with ML researchers. Success after year one looks like pre-training data pipelines that run within SLA without anyone paging you, researchers who trust your data versioning enough to stop requesting one-off Spark jobs, and at least one self-serve system (like an automated data mix assembler) that replaced a manual workflow.

A Typical Week

The coding share is unusually high for a data engineering role at this seniority. That reflects a build-over-buy philosophy: you're writing Spark transformations and constructing Airflow DAGs, not configuring managed services. Meetings stay low because the org is flat and most coordination happens asynchronously over Feishu (Lark), which frees up long blocks for deep pipeline work on Tuesdays and Thursdays.

Projects & Impact Areas

The lakehouse platform anchors everything, storing training corpora as versioned Parquet shards with lineage tracking that matters because DeepSeek follows an open-weight release strategy and needs clear provenance for each dataset. RAG retrieval infrastructure and fine-tuning data preparation pull you in different directions: one demands low-latency ingestion workflows, while the other requires you to orchestrate raw instruction-tuning and math reasoning datasets into clean, reproducible formats based on ablation feedback from ML researchers.

Skills & What's Expected

The skill scores show software engineering, data architecture, and GenAI literacy all rated at expert level, but here's the implication candidates miss: deep GenAI knowledge is what separates you from a strong data engineer at a non-AI company. You need to understand why a chunking strategy change in a retrieval pipeline degrades output quality, or how embedding pipelines interact with the data you serve. Cloud platform experience (AWS, GCP, Azure) is rated high and does matter, but knowing how to optimize Spark jobs and design schemas for both structured metadata and unstructured corpus data will carry you further in this specific role.

Levels & Career Growth

With roughly 200 employees and a flat org, seniority here is about scope of ownership rather than team size. The gap between levels comes down to whether you can design systems that other engineers build on top of, versus owning a single pipeline end to end. Building self-serve tooling that removes you from the critical path (so researchers stop filing ad-hoc data requests through Feishu) is the clearest signal you're operating at the next level.

Work Culture

Work is on-site at the Hangzhou office, with remote arrangements uncommon according to what candidates report. The pace is intense, especially when a new training run needs data on a compressed timeline, and the day-in-life culture notes confirm that data engineers get pulled into urgent requests regularly. The upside is real: you ship fast, decisions don't require a six-week approval chain, and the small team size means your design docs turn into production systems quickly.

DeepSeek Data Engineer Compensation

DeepSeek likely offers equity in the form of RSUs on a standard 4-year vesting schedule, with roughly 25% vesting each year. The initial RSU grant is one of your strongest negotiation levers, so push hard on that number during the offer stage rather than accepting the first figure and hoping for generous refresh grants down the line.

Base salary is the other flexible lever worth pressing. If you have competing offers, use them to anchor your ask on total compensation (base plus RSUs plus bonus) rather than fixating on any single component. Performance-based bonuses round out the package, but from what candidates report, the upfront RSU grant and base are where you'll move the needle most.

DeepSeek Data Engineer Interview Process

Plan for about five weeks end to end across six rounds. The most common reason candidates wash out, from what the data suggests, is lack of depth in system design. Interviewers probe your ability to architect scalable data pipelines and distributed storage solutions, and surface-level answers about technology choices without discussing tradeoffs (fault tolerance vs. cost, batch vs. streaming latency) won't cut it.

The Bar Raiser round is worth special attention. A senior engineer or manager from outside your prospective team evaluates your overall fit, which means winning over the hiring manager alone isn't sufficient. Expect open-ended questions that test how you handle ambiguity and whether you can drive work independently, so prepare stories about self-directed projects, not just technically polished ones.

DeepSeek Data Engineer Interview Questions

with successful as (
  select
    date_trunc('day', requested_at) as day,
    request_id,
    model,
    requested_at,
    coalesce(prompt_tokens, 0) as prompt_tokens,
    coalesce(completion_tokens, 0) as completion_tokens
  from inference_requests
  where status = 'success'
),
ranked as (
  select
    day,
    request_id,
    model,
    prompt_tokens,
    completion_tokens,
    row_number() over (
      partition by day, request_id
      order by requested_at desc
    ) as rn
  from successful
)
select
  day,
  model,
  sum(prompt_tokens + completion_tokens) as successful_tokens
from ranked
where rn = 1
group by 1, 2
order by 1, 2;

from __future__ import annotations

from dataclasses import dataclass
from datetime import datetime
from typing import Any, Dict, Iterable, List, Optional, Tuple


def _parse_event_time(value: Any) -> datetime:
    """Parse event_time into a datetime.

    Accepts:
      - datetime
      - ISO 8601 strings, including a trailing 'Z'
      - int or float as Unix seconds

    Raises ValueError for unsupported formats.
    """
    if isinstance(value, datetime):
        return value
    if isinstance(value, (int, float)):
        return datetime.fromtimestamp(value)
    if isinstance(value, str):
        s = value.strip()
        # Support common ISO format with 'Z'.
        if s.endswith("Z"):
            s = s[:-1] + "+00:00"
        try:
            return datetime.fromisoformat(s)
        except ValueError as e:
            raise ValueError(f"Invalid event_time string: {value!r}") from e
    raise ValueError(f"Unsupported event_time type: {type(value).__name__}")


def dedupe_chat_batch(records: Iterable[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Deduplicate records by (conversation_id, message_id).

    For each key, keeps the record with the largest event_time.
    If event_time ties, keeps the first encountered record to preserve stability.

    Returns survivors in the order they originally appeared.
    """
    # Store index of the winning record for each key.
    winner_index: Dict[Tuple[Any, Any], int] = {}
    # Store parsed event_time for the current winner.
    winner_time: Dict[Tuple[Any, Any], datetime] = {}

    materialized: List[Dict[str, Any]] = []

    for idx, rec in enumerate(records):
        materialized.append(rec)

        try:
            key = (rec["conversation_id"], rec["message_id"])
        except KeyError as e:
            raise KeyError(f"Missing required key: {e.args[0]}") from e

        t = _parse_event_time(rec.get("event_time"))

        if key not in winner_index:
            winner_index[key] = idx
            winner_time[key] = t
            continue

        # Keep the record with max event_time.
        if t > winner_time[key]:
            winner_time[key] = t
            winner_index[key] = idx
        # If tie, keep existing winner to preserve original order.

    winning_positions = set(winner_index.values())
    return [r for i, r in enumerate(materialized) if i in winning_positions]


if __name__ == "__main__":
    data = [
        {"conversation_id": "c1", "message_id": "m1", "event_time": "2025-01-01T00:00:01Z", "text": "a"},
        {"conversation_id": "c1", "message_id": "m1", "event_time": "2025-01-01T00:00:02Z", "text": "a2"},
        {"conversation_id": "c1", "message_id": "m2", "event_time": "2025-01-01T00:00:03Z", "text": "b"},
        {"conversation_id": "c2", "message_id": "m1", "event_time": "2025-01-01T00:00:01Z", "text": "x"},
        {"conversation_id": "c1", "message_id": "m1", "event_time": "2025-01-01T00:00:02Z", "text": "a2-dup"},
    ]

    out = dedupe_chat_batch(data)
    # Expect c1/m1 keeps event_time 00:00:02Z, and tie keeps first 00:00:02Z occurrence.
    assert [r["text"] for r in out] == ["a2", "b", "x"]
    print("OK")

from __future__ import annotations

from collections import Counter
from typing import Dict, Iterable, Iterator, List, Tuple


def _iter_runs(sorted_ids: List[int]) -> Iterator[Tuple[int, int]]:
    """Yield (token_id, run_count) for a sorted list of ints."""
    if not sorted_ids:
        return
    prev = sorted_ids[0]
    cnt = 1
    for x in sorted_ids[1:]:
        if x == prev:
            cnt += 1
        else:
            yield prev, cnt
            prev = x
            cnt = 1
    yield prev, cnt


def topk_frequent_tokens_misra_gries(
    docs: Iterable[List[int]],
    k: int,
    *,
    verify_exact: bool = False,
    docs_second_pass: Iterable[List[int]] | None = None,
) -> List[Tuple[int, int]]:
    """Approximate top-k frequent tokens with Misra-Gries.

    Memory: O(k) counters.

    If verify_exact is True, you must provide docs_second_pass (replayable stream)
    to compute exact counts for candidates and return exact top-k.

    Returns a list of (token_id, count_or_estimate), sorted by count desc.
    """
    if k <= 0:
        return []

    # Misra-Gries keeps at most k candidates.
    candidates: Dict[int, int] = {}

    for doc in docs:
        for token_id, run_count in _iter_runs(doc):
            # Feed run_count occurrences into the sketch efficiently.
            # Misra-Gries update for count > 1 can be done in chunks.
            remaining = run_count
            while remaining > 0:
                if token_id in candidates:
                    candidates[token_id] += remaining
                    break
                if len(candidates) < k:
                    candidates[token_id] = remaining
                    break

                # Decrement all by d = min(counter values, remaining).
                # This simulates inserting token_id remaining times and triggering full decrements.
                d = min(min(candidates.values()), remaining)
                to_delete = []
                for t in list(candidates.keys()):
                    candidates[t] -= d
                    if candidates[t] == 0:
                        to_delete.append(t)
                for t in to_delete:
                    del candidates[t]
                remaining -= d

    if not verify_exact:
        # Return the sketch counts, they are not exact.
        return sorted(candidates.items(), key=lambda x: (-x[1], x[0]))[:k]

    if docs_second_pass is None:
        raise ValueError("docs_second_pass must be provided when verify_exact=True")

    # Second pass to compute exact counts only for candidates.
    exact = Counter()
    cand_set = set(candidates.keys())
    for doc in docs_second_pass:
        for token_id, run_count in _iter_runs(doc):
            if token_id in cand_set:
                exact[token_id] += run_count

    return sorted(exact.items(), key=lambda x: (-x[1], x[0]))[:k]


if __name__ == "__main__":
    docs1 = [
        [1, 1, 2, 2, 2, 5],
        [2, 2, 3],
        [1, 4, 4, 4, 4],
    ]
    # Replayable for verification.
    docs2 = [
        [1, 1, 2, 2, 2, 5],
        [2, 2, 3],
        [1, 4, 4, 4, 4],
    ]

    approx = topk_frequent_tokens_misra_gries(iter(docs1), k=2, verify_exact=False)
    exact = topk_frequent_tokens_misra_gries(iter(docs2), k=2, verify_exact=True, docs_second_pass=iter(docs2))
    assert exact[0][0] in (2, 4)
    print("OK")

What stands out isn't any single dominant area but how the top three categories bleed into each other during live interviews. Candidates report that a question starting as a pipeline orchestration problem (say, backfilling Iceberg tables for training corpus ingestion) will pivot mid-conversation into lakehouse partitioning tradeoffs or RAG chunking decisions, so studying each topic in isolation is the single most common prep failure. The distribution also quietly punishes people who spend most of their prep time on SQL and coding drills while neglecting the design-heavy, AI-infrastructure questions that make up the majority of the conversation.

Practice these question types and more at datainterview.com/questions.

How to Prepare for DeepSeek Data Engineer Interviews

DeepSeek's north star, usable intelligence at production cost, creates a data engineering environment where pipeline efficiency isn't a nice-to-have but a budget line item that leadership tracks weekly. The Thoughtworks analysis of DeepSeek's approach highlights how their MoE architecture and multi-head latent attention reduce compute requirements, but someone still has to build the petabyte-scale ingestion, deduplication, and quality-scoring infrastructure that feeds those training runs without blowing the cost advantage.

That "someone" context is what separates a good "why DeepSeek" answer from a forgettable one. Don't talk about wanting to work on impressive AI. Instead, name a specific infrastructure tension their constraints create: for example, how aggressive deduplication at corpus scale saves GPU hours but risks removing valid near-duplicate training examples that improve model robustness. A Stanford analysis of DeepSeek's disruption and founder Liang Wenfeng's own comments on rejecting the "follow" mentality give you concrete talking points about why cost discipline is a technical strategy, not just frugality.

Try a Real Interview Question

| request_id | ts                  | model          | region     | status | latency_ms | input_tokens | output_tokens |
|------------|---------------------|----------------|------------|--------|------------|--------------|---------------|
| r1         | 2026-02-20 10:01:00 | deepseek-r1    | us-east-1  | ok     | 120        | 500          | 800           |
| r2         | 2026-02-20 10:02:00 | deepseek-r1    | us-east-1  | error  | 900        | 300          | 0             |
| r3         | 2026-02-21 09:12:00 | deepseek-r1    | us-east-1  | ok     | 220        | 1000         | 600           |
| r4         | 2026-02-21 11:05:00 | deepseek-v3    | eu-west-1  | ok     | 150        | 200          | 300           |

| model       | region    | price_per_1k_input_usd | price_per_1k_output_usd |
|------------|-----------|--------------------------|--------------------------|
| deepseek-r1| us-east-1 | 0.40                     | 0.60                     |
| deepseek-v3| eu-west-1 | 0.20                     | 0.30                     |
| deepseek-r1| eu-west-1 | 0.45                     | 0.65                     |
| deepseek-v3| us-east-1 | 0.25                     | 0.35                     |

WITH filtered AS (
  SELECT
    DATE_TRUNC('day', r.ts) AS day,
    r.model,
    r.region,
    r.status,
    r.latency_ms,
    r.input_tokens,
    r.output_tokens,
    p.price_per_1k_input_usd,
    p.price_per_1k_output_usd
  FROM inference_requests r
  JOIN model_pricing p
    ON p.model = r.model
   AND p.region = r.region
  WHERE r.ts >= (CURRENT_DATE - INTERVAL '7 days')
), agg AS (
  SELECT
    day,
    model,
    region,
    COUNT(*) AS total_requests,
    SUM(CASE WHEN status <> 'ok' THEN 1 ELSE 0 END) AS error_requests,
    AVG(CASE WHEN status = 'ok' THEN latency_ms END) AS avg_latency_ms_ok,
    SUM(
      (input_tokens / 1000.0) * price_per_1k_input_usd
      + (output_tokens / 1000.0) * price_per_1k_output_usd
    ) AS total_cost_usd
  FROM filtered
  GROUP BY 1, 2, 3
)
SELECT
  a.day,
  a.model,
  a.region,
  a.total_requests,
  ROUND(a.error_requests * 1.0 / NULLIF(a.total_requests, 0), 6) AS error_rate,
  PERCENTILE_CONT(0.50) WITHIN GROUP (ORDER BY f.latency_ms) FILTER (WHERE f.status = 'ok') AS p50_latency_ms_ok,
  PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY f.latency_ms) FILTER (WHERE f.status = 'ok') AS p95_latency_ms_ok,
  ROUND(a.total_cost_usd, 6) AS total_cost_usd
FROM agg a
JOIN filtered f
  ON f.day = a.day
 AND f.model = a.model
 AND f.region = a.region
GROUP BY
  a.day,
  a.model,
  a.region,
  a.total_requests,
  a.error_requests,
  a.total_cost_usd
ORDER BY a.day, a.model, a.region;

DeepSeek's coding round, from what candidates report, leans toward Python problems that resemble real corpus-processing tasks: parsing heterogeneous text formats, computing streaming aggregations over crawl data, or writing efficient I/O for multi-terabyte batch jobs. The problems reward you for thinking about throughput and memory, not for memorizing graph algorithms. Practice similar patterns at datainterview.com/coding.

Test Your Readiness

DeepSeek's interview spans lakehouse design, ML data pipelines, and SQL optimization over training metadata, so knowing which of those areas you're weakest in saves you from wasting prep time. Calibrate with practice questions at datainterview.com/questions.