Cohere Data Engineer Interview Guide

From hundreds of mock interviews, the candidates who bomb Cohere's data engineering loop share one trait: they prep for generic pipeline questions and blank when asked how a misconfigured deduplication step in a web crawl pipeline would affect downstream model training. Cohere's interviewers probe whether you understand the connection between your infrastructure choices and what happens inside a training run.

Cohere Data Engineer Role

You'll build and maintain the data pipelines that curate training datasets for Cohere's foundation models and feed analytics for enterprise API customers, covering everything from multilingual corpus ingestion to Kafka-to-Snowflake plumbing that tracks usage for billing and product decisions. Success after year one means your pipelines run reliably without middle-of-the-night pages, ML researchers trust the data you deliver, and you've shipped at least one meaningful migration (like moving a legacy MongoDB annotation pipeline to Iceberg on S3).

A Typical Week

The time split that catches people off guard is how much of the week goes to infrastructure work that's really "firefighting in disguise": reconciling Iceberg partition metadata that broke overnight, triaging Slack questions from ML engineers about missing catalog entries, writing runbooks so the next on-call person doesn't have to reverse-engineer your fix. Cross-functional syncs with ML researchers shape your architecture decisions more than you'd expect, because their data freshness SLAs (say, a 6-hour window for a corpus refresh) determine whether you're writing batch Airflow DAGs or rearchitecting toward streaming with Kafka and micro-batch Iceberg writes.

Projects & Impact Areas

The highest-visibility work centers on multilingual training data pipelines: curating and deduplicating web corpora, then delivering clean datasets in Iceberg tables partitioned by language and date for Cohere's model training workflows. That pipeline work dovetails with the customer-facing data layer, where you're building Kafka consumers that ingest enterprise API usage events, validate schemas with Pydantic, and land Parquet in S3 for dbt models powering usage dashboards. Data quality frameworks stitch it all together, catching drift or corruption (like a duplicate spike from a misconfigured Glue ETL job) before it poisons a training run or erodes an enterprise customer's confidence.

Skills & What's Expected

At Cohere, your CI/CD pipelines, test coverage, and incident runbooks carry as much weight as your Spark tuning skills, because the role demands expert-level software engineering rigor alongside expert-level data architecture. ML knowledge is rated high (not expert) for a reason: you won't train models, but you need to explain why schema evolution in your Iceberg tables affects downstream RLHF annotation workflows. Candidates who can discuss Kafka consumer group rebalancing in one breath and articulate how data quality impacts model performance in the next are the ones who clear the bar.

Levels & Career Growth

Notice that the Staff band sits below Senior in total comp, which reflects how private-company equity grants can vary wildly by hire date and negotiation rather than following a clean ladder. What separates Senior from Staff isn't pay but scope: Staff engineers own the long-term technical roadmap for the data platform, drive cross-squad initiatives like data contract enforcement, and act as the architecture authority when the team debates migrating off the Glue catalog.

Work Culture

The role is listed as full-time remote, though Cohere's Toronto HQ runs a hybrid office culture (Tuesday through Thursday in-office, per team norms) for those who are local. The pace is startup-fast with IPO-scale ambitions, so scope creep is common and you'll be expected to read the occasional research paper to understand what the ML team actually needs from your pipelines. On-call rotations are real (PagerDuty alerts happen), but the team protects deep work blocks and most engineers work roughly 9:30 to 6 outside on-call weeks.

Cohere Data Engineer Compensation

The four-year vesting schedule with a one-year cliff means your equity grant is entirely back-loaded in year one. Both base salary and equity are the most negotiable components, so don't treat either as fixed when you get your offer. Refresh grants may become available based on performance, but they're not guaranteed at signing, so the initial grant size matters more than candidates tend to assume.

If you're leaving a public company with unvested RSUs, frame your counteroffer around "making whole" that forfeited comp. Signing bonuses are sometimes offered to bridge exactly this kind of gap. The strongest lever is being specific about what you're walking away from, since Cohere's recruiters know private-company equity carries a liquidity discount compared to publicly traded stock.

Cohere Data Engineer Interview Process

The whole loop runs about four weeks from recruiter call to offer. Across the common rejection reasons candidates report, weak technical fundamentals in SQL, Python, and data structures show up most consistently, often surfacing in the coding round before candidates even reach system design. The system design round is its own filter, though: you're asked to architect a scalable data pipeline or system from scratch, and the interviewers push hard on trade-offs around fault tolerance, monitoring, and cloud-native services like S3, BigQuery, or Airflow.

Don't treat Rounds 4 and 5 as interchangeable "behavioral" conversations. Round 4 is a technical deep-dive where you walk through past data engineering projects, discuss data quality strategies, and field scenario-based questions about ETL optimization and supporting ML workflows. Round 5, with the hiring manager, shifts to collaboration style, motivation, and cultural alignment with Cohere's fast-moving, research-adjacent environment. Underpreparing for either one is a common mistake, since poor communication or cultural mismatch are both standalone rejection reasons in Cohere's process.

Cohere Data Engineer Interview Questions

WITH dedup AS (
  SELECT
    event_ts,
    request_id,
    user_id,
    model,
    prompt_tokens,
    completion_tokens,
    latency_ms,
    status,
    ROW_NUMBER() OVER (PARTITION BY request_id ORDER BY event_ts DESC) AS rn
  FROM inference_events
  WHERE event_ts >= CURRENT_TIMESTAMP - INTERVAL '30' DAY
),
latest AS (
  SELECT
    event_ts,
    request_id,
    user_id,
    model,
    prompt_tokens,
    completion_tokens,
    latency_ms,
    status
  FROM dedup
  WHERE rn = 1
)
SELECT
  CAST(event_ts AS DATE) AS event_date,
  COUNT(*) AS total_requests,
  SUM(CASE WHEN status = 'success' THEN 1 ELSE 0 END) AS success_requests,
  -- p95 only over successful requests
  APPROX_PERCENTILE(CASE WHEN status = 'success' THEN latency_ms END, 0.95) AS p95_latency_ms,
  AVG(prompt_tokens + completion_tokens) AS avg_total_tokens
FROM latest
GROUP BY 1
ORDER BY event_date DESC;

from __future__ import annotations

from collections import defaultdict
from typing import Dict, Iterable, List, Tuple


def reconstruct_text_by_request(
    events: Iterable[Tuple[str, int, str]],
    *,
    separator: str = "",
    conflict: str = "error",  # "error" | "keep_first" | "keep_last"
) -> Dict[str, str]:
    """Reconstruct final text per request_id from token events.

    Args:
        events: Iterable of (request_id, token_index, token_str) in arbitrary order.
        separator: String inserted between tokens when concatenating.
        conflict: Behavior when the same (request_id, token_index) appears with
            different token_str values.

    Returns:
        Dict mapping request_id to the reconstructed text.

    Raises:
        ValueError: If conflict == "error" and conflicting duplicates are found.
    """

    # request_id -> token_index -> token_str
    tokens_by_req: Dict[str, Dict[int, str]] = defaultdict(dict)

    for request_id, token_index, token_str in events:
        if token_index in tokens_by_req[request_id]:
            existing = tokens_by_req[request_id][token_index]
            if existing != token_str:
                if conflict == "error":
                    raise ValueError(
                        f"Conflicting token_str for request_id={request_id}, "
                        f"token_index={token_index}: {existing!r} vs {token_str!r}"
                    )
                elif conflict == "keep_first":
                    continue
                elif conflict == "keep_last":
                    tokens_by_req[request_id][token_index] = token_str
                else:
                    raise ValueError(f"Unknown conflict policy: {conflict}")
            # If identical duplicate, ignore.
        else:
            tokens_by_req[request_id][token_index] = token_str

    out: Dict[str, str] = {}
    for request_id, idx_to_tok in tokens_by_req.items():
        parts: List[str] = [idx_to_tok[i] for i in sorted(idx_to_tok.keys())]
        out[request_id] = separator.join(parts)

    return out


if __name__ == "__main__":
    sample = [
        ("r1", 1, "world"),
        ("r1", 0, "hello "),
        ("r1", 1, "world"),  # duplicate
        ("r2", 0, "foo"),
        ("r2", 2, "baz"),
        ("r2", 1, "bar"),
    ]
    print(reconstruct_text_by_request(sample))

The distribution skews heavily toward building and designing rather than querying or algorithming, which tells you Cohere evaluates data engineers more like infrastructure builders than analysts. Production rigor questions compound the difficulty of every other area because sample questions about duplicate Parquet writes shifting token counts or schema changes breaking dbt models for fine-tuning datasets demand the same orchestration and cloud fluency tested elsewhere. Candidates who silo their prep into "coding" and "SQL" buckets while skimming over testing, observability, and safe migration patterns are making the mistake this distribution punishes hardest.

Practice these question types with full solutions at datainterview.com/questions.

How to Prepare for Cohere Data Engineer Interviews

Cohere is betting that specialized, capital-efficient AI beats brute-force scaling for enterprise buyers. The Aya project covers 100+ languages, which means data engineers here wrestle with multilingual corpus curation at a breadth most AI companies never attempt. The Command A technical report gives you a window into what that data infrastructure actually supports.

The company reached $240 million in ARR in 2025 and has been reported to be eyeing a 2026 IPO. That combination of revenue traction and pre-IPO urgency means pipelines are under real production load from enterprise customers, not sitting in a research sandbox.

When interviewers ask "why Cohere," don't say you want to work on LLMs. Instead, reference something concrete: how Cohere's SageMaker integration means data engineers have to think about multi-cloud deployment constraints, or how supporting 100+ languages in Aya creates data quality tradeoffs you'd be excited to tackle. Anchor your answer to a pipeline problem that only exists at this company.

Try a Real Interview Question

| ingest_date | source      | shard_id | tokens | quality_score |
|------------|-------------|----------|--------|---------------|
| 2026-01-01 | web         | s1       | 1500   | 0.72          |
| 2026-01-02 | web         | s2       | 900    | 0.81          |
| 2026-01-03 | web         | s3       | 2200   | 0.78          |
| 2026-01-01 | pubmed      | p1       | 1800   | 0.84          |
| 2026-01-04 | pubmed      | p2       | 1200   | 0.86          |

| ingest_date | source | shard_id | dup_rate |
|------------|--------|----------|----------|
| 2026-01-01 | web    | s1       | 0.12     |
| 2026-01-02 | web    | s2       | 0.05     |
| 2026-01-03 | web    | s3       | 0.20     |
| 2026-01-01 | pubmed | p1       | 0.02     |
| 2026-01-04 | pubmed | p2       | 0.01     |

WITH joined AS (
  SELECT
    i.ingest_date,
    i.source,
    i.shard_id,
    i.tokens,
    i.quality_score,
    c.dup_rate
  FROM ingestion_stats i
  LEFT JOIN shard_quality_checks c
    ON c.ingest_date = i.ingest_date
   AND c.source = i.source
   AND c.shard_id = i.shard_id
), scored AS (
  SELECT
    ingest_date,
    source,
    shard_id,
    tokens,
    quality_score,
    AVG(quality_score) OVER (
      PARTITION BY source
      ORDER BY ingest_date
      ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
    ) AS rolling_avg_quality
  FROM joined
)
SELECT
  ingest_date,
  source,
  shard_id,
  quality_score,
  rolling_avg_quality
FROM scored
WHERE tokens >= 1000
  AND quality_score >= rolling_avg_quality + 0.05
ORDER BY ingest_date, source, shard_id;

Cohere's job listings for data engineering roles emphasize "expert-level software engineering" and production-grade Python, not competitive programming accolades. That bar shows up in the coding round: clean structure, thoughtful error handling, and readable code matter more than shaving milliseconds off an asymptotic bound. Build that muscle at datainterview.com/coding.

Test Your Readiness

Cohere's double behavioral round and ML-infrastructure system design make their loop distinct. Practice both formats at datainterview.com/questions.