Spotify Data Engineer Interview Guide

Spotify's data engineering org is the plumbing behind royalty payments to millions of artists, ad-impression billing, and the recommendation engines serving 675M+ users. From hundreds of mock interviews we've run, candidates who land offers understand they're joining a product engineering culture where the pipeline is the product.

Spotify Data Engineer Role

After year one, success means owning a pipeline domain through its full lifecycle on Spotify's GCP stack: authoring Scio or Spark jobs, orchestrating them in Flyte, writing dbt models that land in BigQuery, and carrying the on-call pager when something breaks. On the Financial Engineering team, that could mean redesigning how royalty calculations flow from raw stream events through multi-party licensing splits (artist, label, distributor, territory) to actual artist payouts. The bar is end-to-end ownership, not just authoring.

A Typical Week

The thing that catches most candidates off guard is the writing and infrastructure load. RFCs proposing a migration from batch Spark to streaming Flink, runbooks documenting failure modes before an on-call handoff, design docs that go through squad review: nobody else produces these for you in Spotify's autonomous squad model. Expect to spend meaningful hours outside your IDE deploying Dataflow jobs, triaging data quality alerts in Slack, and validating BigQuery cost implications.

Projects & Impact Areas

Royalty and licensing pipelines inside Financial Engineering are some of the gnarliest data problems at any tech company, where a single stream event fans out across artist, label, distributor, and territory dimensions with different contractual rules in each. The Revenue Platform team tackles a different kind of complexity: building real-time ad-impression deduplication in Scio on Dataflow, replacing brittle batch workarounds as Spotify's advertising business scales. Cost Platform is a quieter but high-visibility area, focused on optimizing GCP spend and even carbon footprint, which means your pipeline efficiency work has a direct line to the company's operating margin.

Skills & What's Expected

The skill profile rates business acumen and communication as "high," which is unusual for a DE role and tells you something important about what Spotify actually screens for. You'll sit in cross-tribe syncs with finance or ads data science and translate their metric requirements into data architecture decisions. ML knowledge matters at a "medium" level (you feed models, you don't train them day-to-day), but software engineering rigor is where Spotify's bar feels highest: production-grade code with tests, CI/CD pipelines, and clean PR reviews.

Levels & Career Growth

The biggest total comp jump sits between Engineer II and Senior, which signals where Spotify invests most to retain talent. What separates those levels in practice isn't just deeper technical skill; it's leading multi-sprint projects with less supervision and beginning to mentor others. For the Senior-to-Staff leap, the promo criteria explicitly require impact across multiple squads or a tribe, and the guild system (cross-cutting groups like the Data Engineering guild) is the real mechanism for building that visibility.

Work Culture

Spotify's squad model gives you genuine architectural agency: your squad of roughly 6 people picks its own tooling within guardrails and owns its SLAs. That freedom comes with accountability, since there's no centralized platform team to absorb blame when a royalty pipeline misses a freshness threshold. Data Platform squads tend to cluster in-office Tuesday through Thursday, with weeks rarely exceeding 40-42 hours unless on-call goes sideways. The honest downside? Your experience varies heavily by mission, because autonomy across squads can mean inconsistency in processes, tooling maturity, and documentation quality.

Spotify Data Engineer Compensation

Spotify's 3-year vesting schedule means your initial grant depletes a full year before a typical 4-year grant would. That's great for early liquidity, but it puts extra weight on what happens in year four and beyond. Before you sign, ask your recruiter specifically about how Spotify handles equity refreshers, because that answer determines whether your comp holds steady or quietly drops off.

The biggest negotiation lever isn't the grant size, it's level calibration. If you're sitting at 5+ years of experience with a competing offer, make the case for Senior leveling before you discuss dollar amounts. A level bump reshapes the entire comp band in ways that no amount of equity haggling on a lower band can match. Plant that expectation in the recruiter screen, not after the final round when the offer is already drafted against a specific level.

Spotify Data Engineer Interview Process

Most candidates report the loop taking about five weeks, but the real timeline risk is the Case Study and Bar Raiser rounds. These get scheduled last, often with a two-week gap while Spotify coordinates cross-team interviewers. From what candidates report, the most common rejection trigger is failing to connect technical decisions to Spotify-specific business impact, like explaining why a royalty pipeline's SLA matters differently than a playlist recommendation pipeline's freshness target.

The Bar Raiser round is where confident candidates get blindsided. The source data describes it as a "higher-signal evaluation" that pressure-tests seniority and judgment, and interviewers will revisit choices you made in earlier rounds to see if you can defend them under new constraints. Recycling the same behavioral stories you told in Round 3 reads as shallow. Prepare distinct examples for this conversation, ideally ones that show you influencing across squads or making hard calls about operational burden on a pipeline your team owned end-to-end.

Spotify Data Engineer Interview Questions

import collections

def get_top_k_songs(events, k):
    """
    Finds the top K most played songs from a list of play events.

    Args:
        events (list): A list of tuples, where each tuple is (user_id, song_id, timestamp).
        k (int): The number of top songs to return.

    Returns:
        list: A list of the top K song_ids.
    """
    if not events or k <= 0:
        return []

    # Use a Counter to efficiently count song occurrences
    song_counts = collections.Counter(event[1] for event in events)

    # The most_common() method is highly optimized for this exact task
    # It returns a list of (element, count) tuples, sorted by count descending
    top_k_tuples = song_counts.most_common(k)

    # Extract just the song_ids from the tuples
    top_k_songs = [song_id for song_id, count in top_k_tuples]

    return top_k_songs

# Example Usage:
play_events = [
    (1, 'song_A', 1640995200),
    (2, 'song_B', 1640995201),
    (1, 'song_A', 1640995202),
    (3, 'song_C', 1640995203),
    (2, 'song_A', 1640995204),
    (3, 'song_B', 1640995205),
    (4, 'song_D', 1640995206),
    (1, 'song_A', 1640995207),
    (2, 'song_C', 1640995208),
    (3, 'song_B', 1640995209),
]

K = 2
print(f"Top {K} songs: {get_top_k_songs(play_events, K)}") # Expected: ['song_A', 'song_B']

from collections import defaultdict, deque

def find_task_order(tasks):
    """
    Determines a valid execution order for a list of tasks with dependencies.

    Args:
        tasks (dict): A dictionary where keys are task_ids and values are lists of dependency_ids.

    Returns:
        list: A list of task_ids in a valid execution order, or an empty list if a cycle is detected.
    """
    if not tasks:
        return []

    # Adjacency list: dependency -> list of tasks that depend on it
    adj = defaultdict(list)
    # In-degree: task -> number of dependencies
    in_degree = {task: 0 for task in tasks}
    all_tasks = set(tasks.keys())

    for task, dependencies in tasks.items():
        for dep in dependencies:
            if dep not in all_tasks:
                # A dependency is not a valid task, invalid input
                # Or handle as an error depending on requirements
                return []
            adj[dep].append(task)
            in_degree[task] += 1

    # Queue for nodes with no incoming edges (no dependencies)
    queue = deque([task for task, degree in in_degree.items() if degree == 0])
    
    result = []

    while queue:
        current_task = queue.popleft()
        result.append(current_task)

        # Decrement the in-degree of all neighbors
        for dependent_task in adj[current_task]:
            in_degree[dependent_task] -= 1
            if in_degree[dependent_task] == 0:
                queue.append(dependent_task)

    # If the result doesn't include all tasks, there is a cycle
    if len(result) == len(all_tasks):
        return result
    else:
        return [] # Cycle detected

# Example Usage:
task_dependencies = {
    'A': [],
    'B': ['A'],
    'C': ['A'],
    'D': ['B', 'C'],
    'E': ['D']
}
print(f"Valid order: {find_task_order(task_dependencies)}") # Expected: ['A', 'B', 'C', 'D', 'E'] (or another valid order)

# Example with a cycle:
cyclic_dependencies = {
    'A': ['C'],
    'B': ['A'],
    'C': ['B']
}
print(f"Cyclic order: {find_task_order(cyclic_dependencies)}") # Expected: []

WITH StreamLag AS (
  -- Calculate the time difference between the current and previous stream for each user
  SELECT
    user_id,
    stream_ts,
    LAG(stream_ts, 1) OVER (PARTITION BY user_id ORDER BY stream_ts) AS prev_stream_ts
  FROM
    stream_events
),
SessionIdentifier AS (
  -- Identify the start of a new session
  -- A new session starts if it's the user's first stream or if the gap is > 20 minutes
  SELECT
    user_id,
    stream_ts,
    CASE
      WHEN prev_stream_ts IS NULL OR
           TIMESTAMP_DIFF(stream_ts, prev_stream_ts, MINUTE) > 20
      THEN 1
      ELSE 0
    END AS is_new_session
  FROM
    StreamLag
),
SessionGrouping AS (
  -- Assign a unique session ID to each stream event by doing a cumulative sum
  -- of the is_new_session flag
  SELECT
    user_id,
    stream_ts,
    SUM(is_new_session) OVER (PARTITION BY user_id ORDER BY stream_ts) AS session_id
  FROM
    SessionIdentifier
),
SessionDurations AS (
  -- Calculate the duration of each session
  SELECT
    user_id,
    session_id,
    TIMESTAMP_DIFF(MAX(stream_ts), MIN(stream_ts), MINUTE) AS session_duration_minutes
  FROM
    SessionGrouping
  GROUP BY
    1, 2
)
-- Find the longest session for each user
SELECT
  user_id,
  MAX(session_duration_minutes) AS longest_session_minutes
FROM
  SessionDurations
GROUP BY
  1
ORDER BY
  2 DESC;

-- This solution uses BigQuery standard SQL syntax for generating a date series
WITH MonthEnds AS (
  -- Generate the last day of each month for the year 2023
  SELECT
    LAST_DAY(month_start) AS month_end_date
  FROM
    UNNEST(GENERATE_DATE_ARRAY('2023-01-01', '2023-12-31', INTERVAL 1 MONTH)) AS month_start
)
-- Join the month-end dates with the subscriptions table to find the active plan
SELECT
  m.month_end_date,
  s.user_id,
  s.plan_id
FROM
  MonthEnds m
JOIN
  user_subscriptions s
ON
  -- The subscription must have started on or before the month-end date
  s.start_date <= m.month_end_date
  AND (
    -- And the subscription must either be ongoing (end_date is NULL)
    -- or have ended after the month-end date
    s.end_date IS NULL OR s.end_date > m.month_end_date
  )
ORDER BY
  m.month_end_date,
  s.user_id;

-- This query finds the top 5 most followed artists
SELECT
    a.artist_id,
    a.artist_name,
    COUNT(f.user_id) AS follower_count
FROM
    artists a
JOIN
    user_artist_follows f ON a.artist_id = f.artist_id
GROUP BY
    1, 2 -- Group by both artist_id and artist_name
ORDER BY
    follower_count DESC -- Order by the count of followers in descending order
LIMIT 5; -- Limit the result to the top 5

The distribution skews heavily toward design and reasoning over raw coding, which tells you something about how Spotify's loop actually filters candidates. System design questions pull in GCP decisions and data modeling tradeoffs simultaneously, so weakness in either area surfaces fast under a single prompt (like the collaborative playlist or podcast recommendation scenarios shown above). The biggest prep mistake is over-indexing on algorithm drills when the interview rewards you for connecting pipeline architecture to Spotify-specific product contexts, like serving features for Discover Weekly or calculating engagement metrics for new social features.

Practice system design, SQL, and behavioral questions tailored to data engineering roles at datainterview.com/questions.

How to Prepare for Spotify Data Engineer Interviews

Spotify's two fastest-moving fronts for data engineers are advertising and creator payouts. The Spotify Ad Exchange and Ads Manager now serve automated programmatic campaigns across 696 million monthly users, which means pipelines for ad targeting, completion-rate measurement, and attribution are under constant pressure to scale. The creator side paid out over $11 billion in royalties in 2025, flowing through multi-party chains (artist to label to distributor to territory) that make reconciliation a genuinely hard data modeling problem.

On top of that, Spotify is tightening AI content protections to prevent artist impersonation and mismatched content, opening up newer pipeline work around identity verification and content classification. When you're asked "why Spotify," skip the fan pitch. Talk about a specific mission's data problem: the territory-level complexity in royalty reconciliation, the measurement gaps in programmatic ad attribution, or how Backstage signals that Spotify treats internal developer tooling as a first-class product. That's what separates you from someone who just likes Discover Weekly.

Try a Real Interview Question

def sessionize_events(events: list[dict], session_timeout_seconds: int) -> list[dict]:
    """
    Groups user events into sessions based on an inactivity timeout.

    Args:
        events: A list of event dictionaries, each with 'user_id', 'timestamp',
                and 'event_type'. Timestamps are Unix epoch seconds. The list
                is not guaranteed to be sorted.
        session_timeout_seconds: The maximum time in seconds between two
                                 consecutive events in the same session.

    Returns:
        A list of session dictionaries, sorted by user_id and then start_time.
        Each session dictionary should have 'user_id', 'start_time',
        'end_time', and 'event_count'.
    """
    pass

def sessionize_events(events: list[dict], session_timeout_seconds: int) -> list[dict]:
    """
    Groups user events into sessions based on an inactivity timeout.

    Args:
        events: A list of event dictionaries, each with 'user_id', 'timestamp',
                and 'event_type'. Timestamps are Unix epoch seconds. The list
                is not guaranteed to be sorted.
        session_timeout_seconds: The maximum time in seconds between two
                                 consecutive events in the same session.

    Returns:
        A list of session dictionaries, sorted by user_id and then start_time.
        Each session dictionary should have 'user_id', 'start_time',
        'end_time', and 'event_count'.
    """
    if not events:
        return []

    # Sort events by user and then time to process them chronologically per user.
    sorted_events = sorted(events, key=lambda e: (e['user_id'], e['timestamp']))

    sessions = []
    current_session = None

    for event in sorted_events:
        if current_session is None:
            # Start the very first session.
            current_session = {
                'user_id': event['user_id'],
                'start_time': event['timestamp'],
                'end_time': event['timestamp'],
                'event_count': 1
            }
        elif event['user_id'] != current_session['user_id'] or \
             event['timestamp'] > current_session['end_time'] + session_timeout_seconds:
            # Finalize the previous session and start a new one.
            # This happens if the user changes or the timeout is exceeded.
            sessions.append(current_session)
            current_session = {
                'user_id': event['user_id'],
                'start_time': event['timestamp'],
                'end_time': event['timestamp'],
                'event_count': 1
            }
        else:
            # Extend the current session.
            current_session['end_time'] = event['timestamp']
            current_session['event_count'] += 1

    # Append the last session after the loop finishes.
    if current_session:
        sessions.append(current_session)

    return sessions

Spotify has leaned on Python since its earliest engineering days, so expect coding rounds to reward clean, testable code over brute-force algorithmic cleverness. Problems that involve parsing nested structures or aggregating event-style data are a good proxy for the kind of work royalty and ad-impression pipelines demand. Sharpen that muscle at datainterview.com/coding with medium-to-hard Python problems focused on real data wrangling patterns.

Test Your Readiness

Pair this quiz with datainterview.com/questions to simulate the case study round, where you'll need to reason about Spotify-specific tradeoffs like freshness SLAs for financial reporting versus playlist recommendations.