Lyft Data Engineer Interview Guide

Lyft Data Engineer candidates tend to prep like it's a SQL-and-pipelines role, then get caught off guard by the software engineering expectations. You'll write production Python with real tests, own on-call for your own tables, and ship design docs that go through cross-team technical review. The bar is closer to a backend engineer who specializes in data than a traditional ETL developer.

Lyft Data Engineer Role

At Lyft, data engineers build and maintain the Airflow DAGs, PySpark transformations in Databricks, and Snowflake data models that power everything from dynamic surge pricing to driver ETA predictions. You're not handing off to a separate platform team. Success after year one looks like shipping an end-to-end pipeline (say, the AV telemetry medallion architecture or a Hive-to-Databricks migration), earning trust from the ML and analytics teams who consume your tables, and running clean on-call rotations where incidents get documented, not just patched.

A Typical Week

The widget shows the time split, but what it can't convey is how reactive the infrastructure work feels. Monday SLA triage and Friday on-call handoffs bookend the week, and the deep building happens Tuesday through Thursday, if you protect your calendar from Slack requests about missing tables and slow Trino queries.

Projects & Impact Areas

Pricing data engineering feeds the real-time surge models that riders see every day, while the Mapping team runs geospatial ETL for route optimization with tighter latency requirements and messier schemas. AV telemetry pipelines (bronze-to-silver-to-gold in Databricks) are newer and still being shaped, which means more greenfield design work. Central Data and Corporate Data & Analytics handle shared warehouse tooling and internal reporting, so the flavor of work varies dramatically depending on which team you join.

Skills & What's Expected

SQL is tested and expected, but it won't differentiate you. Production Python is what separates candidates who advance from those who don't. The role's preferred skills include vector databases and agentic applications for data engineering, which signals where Lyft is heading, so familiarity with embedding pipelines or feature stores gives you an edge even though you won't train models yourself. Cloud-wise, the stack is AWS (S3, DynamoDB), Databricks, and Snowflake. Don't spend prep time on GCP or Azure.

Levels & Career Growth

The widget shows the level bands. The required experience floor is 5+ years, and the job descriptions emphasize independent delivery at the lower end versus cross-team framework and governance impact at the upper end. That governance piece is the promotion blocker most people underestimate: building reliable pipelines for your own team is necessary but not sufficient to move up.

Work Culture

Lyft expects three-plus days per week in the San Francisco office, with Tuesday through Thursday as the standard in-office stretch. The engineering org values open-source contribution (Amundsen is a Lyft project) and internal writing. Publishing on the eng blog or improving shared tooling counts toward promotion cases, which is unusual enough to be worth noting if you're comparing offers.

Lyft Data Engineer Compensation

Lyft's single-year RSU vesting means your year-one TC and year-two TC look nearly identical, with no built-in equity ramp. Because Lyft's RSU structure is less flexible than most negotiation levers, your energy is better spent pushing on base salary and signing bonus, where recruiters have more room to move.

If you're holding a competing offer that includes an annual performance bonus, say so explicitly. Lyft doesn't offer performance bonuses, so recruiters understand they need to compensate elsewhere. The tactic most candidates miss: quantify the gap between your competitor's four-year equity package and Lyft's one-year plan, then ask for a larger sign-on bonus to bridge that difference rather than arguing over RSU grant size.

Lyft Data Engineer Interview Process

The loop spans about six weeks from recruiter call to offer, with eight distinct rounds. That's a lot of surface area for evaluation, and the take-home assignment (which candidates sometimes receive mid-process) adds calendar time since you'll need a few hours to build a small pipeline, write tests, and document your work before the team reviews it.

The top reason candidates get rejected? Underprepping algorithms. Lyft includes two separate coding rounds, and the second one covers graph traversal, dynamic programming, and other problems that feel more like a software engineer interview than a data engineer one. Candidates who load all their prep into SQL and pipeline design often hit a wall on that second coding session. Because every interviewer's score gets weighed in the final decision, you can't offset a weak algorithms round with a strong system design showing. A mediocre mark on any single round stays in the record and counts against you.

Lyft Data Engineer Interview Questions

/*
Assumptions:
- status values include 'online' and other values (e.g., 'offline').
- "Driver cancel" means canceled_by = 'driver'.
- "Accepted" means accepted_at is not null.
- Week starts on Monday. In Snowflake, DATE_TRUNC('WEEK', ...) returns Monday-based weeks.
*/

WITH rides_base AS (
  SELECT
    r.ride_id,
    r.city_id,
    r.driver_id,
    r.requested_at,
    r.accepted_at,
    r.canceled_at,
    r.canceled_by,
    DATE_TRUNC('WEEK', r.requested_at) AS week_start
  FROM rides r
  WHERE r.requested_at IS NOT NULL
),
status_asof AS (
  SELECT
    rb.ride_id,
    rb.city_id,
    rb.week_start,
    rb.accepted_at,
    rb.canceled_by,
    ds.status,
    ROW_NUMBER() OVER (
      PARTITION BY rb.ride_id
      ORDER BY ds.status_ts DESC
    ) AS rn
  FROM rides_base rb
  JOIN driver_status ds
    ON ds.driver_id = rb.driver_id
   AND ds.status_ts <= rb.requested_at
)
SELECT
  s.week_start,
  s.city_id,
  /* Only rides where the driver was online at request time */
  SUM(CASE WHEN s.status = 'online' AND s.canceled_by = 'driver' THEN 1 ELSE 0 END) AS cancels,
  SUM(CASE WHEN s.status = 'online' AND s.accepted_at IS NOT NULL THEN 1 ELSE 0 END) AS accepted,
  /* Avoid divide-by-zero and make the rate deterministic */
  SAFE_DIVIDE(
    SUM(CASE WHEN s.status = 'online' AND s.canceled_by = 'driver' THEN 1 ELSE 0 END),
    NULLIF(SUM(CASE WHEN s.status = 'online' AND s.accepted_at IS NOT NULL THEN 1 ELSE 0 END), 0)
  ) AS cancel_rate
FROM status_asof s
WHERE s.rn = 1
GROUP BY 1, 2
ORDER BY 1, 2;

from __future__ import annotations

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


@dataclass(frozen=True)
class TripEvent:
    trip_id: str
    event_type: str
    ts: int  # Unix epoch seconds, or any comparable integer timestamp


# Higher number means higher precedence when timestamps tie.
_PRECEDENCE: Dict[str, int] = {
    "requested": 0,
    "accepted": 1,
    "canceled": 2,
    "completed": 3,
}


def final_status_by_trip(events: Iterable[TripEvent]) -> Dict[str, str]:
    """Return final status for each trip based on latest timestamp.

    Rules:
      1) Pick the event with the maximum ts per trip_id.
      2) If multiple events share the same max ts, pick by precedence:
         completed > canceled > accepted > requested.
      3) Unknown event types are ignored.

    Time: O(n), Space: O(k) where k is number of unique trip_ids.
    """

    # Store (best_ts, best_precedence, best_status)
    best: Dict[str, Tuple[int, int, str]] = {}

    for e in events:
        if e.event_type not in _PRECEDENCE:
            # In real pipelines you might log or count these, but do not crash.
            continue

        cand = (e.ts, _PRECEDENCE[e.event_type], e.event_type)
        cur = best.get(e.trip_id)

        if cur is None:
            best[e.trip_id] = cand
            continue

        # Compare by ts first, then precedence.
        if cand[0] > cur[0] or (cand[0] == cur[0] and cand[1] > cur[1]):
            best[e.trip_id] = cand

    # Materialize output dict.
    return {trip_id: status for trip_id, (_, __, status) in best.items()}


if __name__ == "__main__":
    sample = [
        TripEvent("t1", "requested", 100),
        TripEvent("t1", "accepted", 105),
        TripEvent("t1", "canceled", 110),
        TripEvent("t1", "completed", 110),  # tie on ts, completed wins
        TripEvent("t2", "requested", 200),
        TripEvent("t2", "accepted", 190),  # out of order, ignored by ts
        TripEvent("t3", "weird", 1),  # unknown type, ignored
    ]

    assert final_status_by_trip(sample) == {"t1": "completed", "t2": "requested"}
    print("OK")

The heavy tilt toward pipeline and system design creates a compounding problem most candidates don't anticipate: Lyft's system design prompts (like sketching a near-real-time Trip Events table with hourly city-level SLAs) require you to reason fluently about Airflow DAG dependencies, late-arriving ride events, and backfill mechanics from the pipeline side. You can't fake that overlap by memorizing generic distributed systems patterns. Candidates who've prepped on Lyft's actual stack (Flyte for orchestration, Databricks writing partitioned Parquet to S3, Snowflake external tables) will sound fundamentally different from those drawing abstract boxes and arrows.

Drill pipeline, modeling, and system design questions built around ride-hailing scenarios at datainterview.com/questions.

How to Prepare for Lyft Data Engineer Interviews

Lyft is hiring aggressively again. Headcount jumped roughly 33% year-over-year to 3,913 employees, and the company posted record Q4 and full-year 2025 results with $6.3B in revenue. For data engineers, that growth translates into concrete new pipeline surface area: the Benteler autonomous shuttle integration needs telemetry ingestion from a completely different vehicle type, and teen accounts introduce new safety-event schemas that feed real-time tracking and PIN verification systems.

Most candidates blow the "why Lyft" question by talking about the mission in vague terms. What lands instead: reference the Q4 2025 prepared remarks and the push to build a hybrid rideshare network blending human drivers with AVs. Then explain why unifying AV telemetry and human-driver event streams into a single analytics layer is a data engineering problem you'd want to own.

Try a Real Interview Question

| trip_events |
|-------------|
| trip_id | event_ts            | status     | city_id |
|---------|---------------------|------------|---------|
| t1      | 2026-02-18 23:50:00 | requested  | 10      |
| t1      | 2026-02-19 00:10:00 | completed  | 10      |
| t2      | 2026-02-19 12:00:00 | requested  | 10      |
| t2      | 2026-02-21 09:00:00 | canceled   | 10      |
| t3      | 2026-02-20 08:30:00 | requested  | 11      |

| city_dim |
|----------|
| city_id | city_name      | region |
|---------|----------------|--------|
| 10      | San Francisco  | west   |
| 11      | Chicago        | midwest|
| 12      | New York       | east   |

WITH params AS (
  SELECT CAST('2026-02-20' AS DATE) AS run_date
),
lookback AS (
  SELECT
    e.trip_id,
    e.event_ts,
    e.status,
    e.city_id
  FROM trip_events e
  CROSS JOIN params p
  WHERE e.event_ts >= CAST(p.run_date - INTERVAL '2' DAY AS TIMESTAMP)
    AND e.event_ts <  CAST(p.run_date + INTERVAL '1' DAY AS TIMESTAMP)
),
ranked AS (
  SELECT
    trip_id,
    event_ts,
    status AS final_status,
    city_id,
    ROW_NUMBER() OVER (PARTITION BY trip_id ORDER BY event_ts DESC) AS rn
  FROM lookback
)
SELECT
  CAST(event_ts AS DATE) AS event_date,
  final_status,
  COUNT(*) AS trips
FROM ranked
WHERE rn = 1
GROUP BY 1, 2
ORDER BY 1, 2;

Lyft's coding rounds reward candidates who can manipulate event-level data in Python, not whiteboard abstract algorithms. Problems tend to resemble the kind of work you'd do extending Flyte workflows or processing ride-event partitions in Spark. Build timed reps at datainterview.com/coding so you're not losing minutes to syntax under pressure.

Test Your Readiness

Lyft's question mix skews heavily toward pipeline orchestration and system design. Drill those areas alongside SQL and data modeling at datainterview.com/questions.