Spotify Data Analyst Interview Preparation Guide - Junior Level
Spotify's Data Analyst interview process for junior-level candidates is comprehensive and spans 4-6 weeks. It consists of a recruiter screening call, followed by two technical phone interviews focusing on SQL and Python/Analytics skills, and four onsite rounds covering case study analysis, product analytics deep dive, data communication and visualization, and team/culture fit assessment. The process is designed to evaluate technical proficiency in data manipulation and analysis, problem-solving ability with real-world datasets, communication effectiveness across audiences, and alignment with Spotify's culture and mission to unlock human creativity through data-driven insights.
Interview Rounds
Recruiter Screening
What to Expect
This is your first interaction with Spotify's recruitment team. A recruiter will conduct a 30-minute call to assess your background, professional experiences, and initial fit for the Data Analyst role. The recruiter will discuss your motivations for joining Spotify, your understanding of the role and company, and your alignment with Spotify's culture and values. They will explain the interview process timeline, set expectations for technical assessments, and answer your questions about the role and company. This stage focuses on confirming baseline qualifications and assessing cultural fit before progressing to technical evaluations.
Tips & Advice
Research Spotify thoroughly before this call, going beyond surface-level knowledge. Prepare 2-3 specific examples demonstrating your passion for data-driven decision-making and understanding of the music streaming or technology industry. Show enthusiasm about Spotify's mission to unlock creator potential and enhance fan engagement. Be concise but personable—recruiters appreciate authenticity. Have concrete examples ready demonstrating: a time you cleaned messy data and extracted valuable insights, a project where SQL or Python directly solved a business problem, and collaboration with non-technical stakeholders. Ask thoughtful questions about the team structure, key projects, and data infrastructure. Confirm your availability for subsequent rounds and clarify any logistical details. Prepare to discuss your experience with the specific tools mentioned in the job description: SQL, Python, Excel, Tableau or Power BI. Articulate why Spotify specifically appeals to you—avoid generic responses applicable to any tech company.
Focus Topics
Spotify Culture and Values Alignment
Understanding and genuine alignment with Spotify values including bias-to-action, collaboration, creativity, diversity, and mission-driven work; examples from your background demonstrating these values
Practice Interview
Study Questions
Understanding of Data Analyst Role
Clear comprehension of daily responsibilities: data collection and cleaning, SQL query writing, statistical analysis, creating dashboards and reports, ad-hoc analysis, collaborating with cross-functional teams, and presenting findings to stakeholders
Practice Interview
Study Questions
Technical Skills Overview
High-level summary of proficiency with SQL (query writing, database familiarity), Python (pandas, data analysis libraries), Excel (pivot tables, analysis), Tableau or Power BI (dashboard creation), and statistical analysis experience
Practice Interview
Study Questions
Background and Relevant Experience
Overview of your 1-2 years of data analysis experience, completed projects involving SQL and Python, familiarity with tools mentioned in job description (Tableau, Power BI, Excel), and demonstrated growth in analytical capabilities
Practice Interview
Study Questions
Motivation for Spotify
Your genuine interest in Spotify specifically—understanding of their mission, products, market position, recent initiatives, and why this role and company appeal to you beyond compensation
Practice Interview
Study Questions
Technical Phone Screen - SQL
What to Expect
This 60-minute technical phone interview assesses your SQL proficiency and ability to extract, transform, and analyze data from relational databases. You'll be presented with data schemas related to Spotify's business domain (user tables, streaming history, playlists, artist information) and asked to write SQL queries answering specific analytical questions. The evaluation focuses on query correctness, code clarity, optimization thinking, and your ability to explain approach before coding. At the junior level, expect questions covering table joins (INNER, LEFT, RIGHT), WHERE clause filtering, GROUP BY with aggregation functions, basic window functions, and understanding of query performance implications. You may be asked to optimize queries or discuss trade-offs between query complexity and readability.
Tips & Advice
Practice on DataLemur and LeetCode focusing on Spotify-specific scenarios: analyzing user streaming patterns, calculating playlist popularity metrics, tracking artist engagement trends, and user retention cohorts. Master INNER/LEFT/RIGHT OUTER joins and understand the precise difference between each—when to use each is critical. Become fluent with subqueries and CTEs (Common Table Expressions) for complex multi-step analysis. Study window functions (ROW_NUMBER, RANK, LAG, LEAD, SUM OVER PARTITION BY) for analyzing time-series data and calculating rolling metrics like 7-day average listens. Think out loud as you write—explain your schema assumptions, your approach to the problem, and why you're using specific SQL constructs before implementing. Test your logic with sample data mentally. Optimize for readability first (clear variable names, proper formatting, comments), then performance. Always ask clarifying questions about the data structure if schemas aren't provided. If you get stuck, walk through the problem step-by-step rather than guessing. Discuss edge cases: what if data is incomplete, how would NULL values be handled, what about duplicate records. A good solution you clearly explain is better than a perfect solution you stumble through.
Focus Topics
Query Optimization and Performance Thinking
Understanding query execution and performance implications; recognizing index usage impact; discussing optimization strategies for large datasets; balancing query efficiency with code readability; understanding limitations of data volume at scale
Practice Interview
Study Questions
Window Functions for Time-Series Analysis
Using ROW_NUMBER, RANK, DENSE_RANK for ranking; LAG/LEAD for comparing current vs. previous periods; SUM/AVG with OVER clause for running totals and rolling averages; PARTITION BY for segment-level calculations
Practice Interview
Study Questions
Data Extraction for User Behavior Analysis
Writing queries to extract user listening history, stream counts, playlist interactions, and engagement patterns; filtering by time periods, user segments, genres, or other dimensions; answering business questions about user behavior
Practice Interview
Study Questions
SQL JOIN Operations
Mastering INNER, LEFT, RIGHT, and FULL OUTER joins; understanding join conditions and result set differences; combining multiple tables to create comprehensive datasets; recognizing when each join type is appropriate
Practice Interview
Study Questions
SQL Aggregation and Grouping
Using GROUP BY with aggregate functions (COUNT, SUM, AVG, MAX, MIN); HAVING clauses for filtering aggregated results; calculating business metrics like daily active users, average session length, revenue by tier, and popularity rankings
Practice Interview
Study Questions
Technical Phone Screen - Python and Analytics
What to Expect
This 60-minute technical interview evaluates your Python programming skills for data analysis and statistical understanding. You'll write Python code (using pandas, NumPy, or scikit-learn) to clean datasets, perform exploratory data analysis, and extract insights. You may also encounter statistical scenarios requiring interpretation of results, understanding bias, or analyzing experimental data. At the junior level, expect practical data manipulation questions rather than advanced machine learning—focus on data cleaning, aggregation, analysis, and correct statistical interpretation. The interviewer assesses both your coding ability and conceptual understanding of when and why to use specific statistical approaches.
Tips & Advice
Practice Python data manipulation with pandas extensively: reading various data formats, handling missing values and duplicates, filtering rows, creating new columns, grouping and aggregating, merging dataframes. Write clean, well-commented code—clarity matters as much as correctness. Explain your approach before coding. Understand statistical fundamentals: mean, median, standard deviation, normal distribution, hypothesis testing, p-values, and confidence intervals. Know when to use t-tests vs. chi-square tests vs. correlation analysis. Study A/B testing: experimental design, power analysis, interpreting results, identifying confounding factors. Practice with real datasets similar to Spotify's (user engagement, streaming patterns, subscription data). Walk through your code step-by-step on paper before running it. Discuss potential data quality issues you'd watch for. Don't just code—explain the business context and what insight you're trying to extract. Be prepared to discuss results: 'What does this p-value mean? When would we reject the null hypothesis? How confident are we in this conclusion?' Strong candidates explain not just what their code does but why they chose that approach.
Focus Topics
A/B Testing and Experimentation Frameworks
Understanding A/B test design and assumptions, power analysis and sample size calculation, interpreting test results and statistical significance, identifying confounding variables, translating statistical findings into business recommendations
Practice Interview
Study Questions
Problem-Solving and Analytical Approach
Breaking ambiguous questions into specific, testable hypotheses; selecting appropriate data and analytical methods; validating assumptions; discussing limitations and trade-offs; explaining findings clearly to different audiences
Practice Interview
Study Questions
Exploratory Data Analysis (EDA)
Understanding dataset structure and characteristics through descriptive statistics, data distribution analysis, identifying outliers and anomalies, recognizing data quality issues, visualizing patterns and relationships
Practice Interview
Study Questions
Statistical Analysis and Hypothesis Testing
Understanding null/alternative hypotheses, p-values and statistical significance, confidence intervals, Type I and Type II errors, selecting appropriate statistical tests, interpreting results in business context
Practice Interview
Study Questions
Python Data Manipulation with Pandas
Reading data from various sources, handling missing and duplicate values, filtering/subsetting data, creating derived columns, grouping and aggregating, merging/joining dataframes, reshaping data structures for analysis
Practice Interview
Study Questions
Case Study Analysis - Onsite
What to Expect
This 90-minute onsite round presents a realistic Spotify business scenario requiring end-to-end analytical thinking and problem-solving. You may receive a dataset (playlist performance, user engagement, artist trends, subscription analysis) and be asked to frame the problem, define success metrics, perform analysis, and provide actionable recommendations. Alternatively, you might be given a business question with limited data and asked how you'd approach analysis. You'll work on a laptop with a whiteboard, discussing your approach with an interviewer in real-time. This round evaluates your ability to think systematically, translate ambiguous questions into measurable objectives, execute analysis efficiently, and communicate findings persuasively. It's as much about your process and thinking as the final results.
Tips & Advice
Start by clarifying the business question—ask what problem you're solving, who needs this answer, and how they'll use the findings. Define your approach explicitly before diving into data. Spend 5-10 minutes exploring the dataset: What's the structure? How many rows? What data quality issues exist? What time periods does it cover? Then frame your hypothesis and metrics. Execute analysis methodically: exploratory analysis → metric calculation → visualization → insights → recommendations. Create 2-3 clear visualizations that illuminate key findings. Connect every analytical finding back to business impact—'If we improve retention by 2%, what's the revenue impact?' Practice case studies using DataLemur, CaseCoach, or strataScratch with Spotify/tech company scenarios. Don't pursue perfection—interviewers value thinking process over flawless results. If stuck, talk through your logic out loud rather than silencing. Ask clarifying questions if direction isn't clear. Discuss trade-offs and limitations of your approach. Reference Spotify's real business dynamics in your analysis (e.g., 'This retention improvement is important because LTV impact at scale could increase annual revenue by $X million').
Focus Topics
Handling Ambiguity and Data Constraints
Asking clarifying questions when requirements are vague; working effectively with incomplete or imperfect data; making reasonable assumptions and stating them explicitly; discussing limitations and alternative interpretations; maintaining analysis rigor despite constraints
Practice Interview
Study Questions
Data Storytelling and Visualization
Presenting findings through clear, compelling visualizations and narratives; choosing appropriate chart types; highlighting key insights prominently; using color and design intentionally; explaining complex results to different audiences; connecting findings to business implications
Practice Interview
Study Questions
Recommendations and Business Impact
Translating analytical findings into specific, actionable recommendations; estimating potential business impact with numbers; considering implementation feasibility and trade-offs; identifying next steps or follow-up analyses
Practice Interview
Study Questions
Data Analysis and Insights Extraction
Performing systematic analysis to uncover patterns, trends, and correlations; identifying outliers and anomalies; drawing valid conclusions from data; acknowledging limitations and alternative explanations; quantifying findings with concrete numbers
Practice Interview
Study Questions
Metrics and KPI Definition
Identifying and defining relevant metrics for the specific question: engagement metrics (DAU, session length), retention metrics (churn rate, cohort retention), revenue metrics (ARPU, LTV), segment-specific metrics; connecting metrics to business objectives
Practice Interview
Study Questions
Problem Definition and Framing
Understanding the business question fully, clarifying ambiguities and scope, defining success metrics before analysis, structuring analytical approach, identifying data needs and potential limitations
Practice Interview
Study Questions
Product Analytics Deep Dive - Onsite
What to Expect
This 60-minute onsite interview focuses on product analytics expertise and understanding of Spotify's business. You'll discuss Spotify's key business metrics (DAU, churn rate, retention, ARPU, engagement), product strategy, and how analytics informs product decisions. You may analyze hypothetical product changes, define success metrics for new features, or diagnose product health issues using metrics. The interviewer assesses your understanding of Spotify's business model (subscription tiers, ad-supported options, creator partnerships), product offerings (music, podcasts, playlists, personalization), and user behavior patterns. This round evaluates product thinking, business acumen, and the ability to connect technical analysis to strategic outcomes. You'll be expected to think like both an analyst and product strategist.
Tips & Advice
Research Spotify's business model deeply: How do subscriptions and ads generate revenue? What are the different pricing tiers? How do they compensate artists and creators? What recent product changes have they announced (podcast integration, new features, market expansion)? Understand user segments: free tier, premium, podcast listeners, creators/artists. Master key metrics: Daily Active Users (DAU), churn rate, retention cohorts, lifetime value (LTV), Average Revenue Per User (ARPU), session length, feature engagement. Think about what metrics matter for different product decisions. Practice answering: 'How would you measure success for a new recommendation feature?' or 'User engagement dropped 3% this quarter—how would you investigate?' Reference Spotify's real products—mention specific features you use and think about how they'd be measured. Understand the business tension: personalization improves engagement but artists prefer discovery; free tier grows users but may cannibalize premium. Show you understand these trade-offs. Differentiate Spotify from competitors like Apple Music or YouTube Music in terms of metrics and strategy. Discuss how data shapes product at Spotify.
Focus Topics
Spotify's Product and Competitive Positioning
Familiarity with Spotify's platform (music discovery, playlists, podcasts, artist tools, recommendation algorithms), different user segments and their needs, competitive positioning vs. Apple Music/YouTube Music, recent product initiatives and market expansion
Practice Interview
Study Questions
Revenue Metrics and Monetization
Average Revenue Per User (ARPU), subscription conversion rates, ad revenue calculations, pricing tier mix analysis, and connecting product changes to revenue impact; understanding LTV and customer acquisition cost dynamics
Practice Interview
Study Questions
Product Analytics Framework
Defining success metrics before launching features, setting up measurement infrastructure, analyzing adoption and impact post-launch, connecting metrics to strategic objectives, running experiments to validate impact
Practice Interview
Study Questions
Spotify Business Model and Revenue Streams
Understanding Spotify's revenue sources (premium subscriptions, ad-supported tier, creator/artist partnerships); different user tiers and their financial impact; how business model shapes analytics priorities and product strategy
Practice Interview
Study Questions
Retention, Churn, and Lifetime Value
Measuring retention rates and cohort analysis, understanding churn drivers and patterns, calculating lifetime value (LTV), identifying retention levers through feature analysis, understanding subscription renewal dynamics
Practice Interview
Study Questions
Engagement Metrics and User Behavior
Daily Active Users (DAU), session length and frequency, feature adoption rates, listening patterns by genre/artist, playlist interactions, and how engagement metrics vary by user segment and platform
Practice Interview
Study Questions
Data Communication and Visualization - Onsite
What to Expect
This 60-minute onsite interview assesses your ability to communicate data findings clearly and create effective visualizations for diverse audiences. You'll work with datasets and be asked to create dashboards, reports, or presentation visualizations for both technical and non-technical stakeholders. You may use Tableau, Power BI, Excel, or other tools to bring data to life. The interviewer evaluates your visualization choices, ability to tell coherent data stories, design clarity, and effectiveness in translating analysis into actionable insights. This round recognizes that great analysis is worthless if stakeholders don't understand it—communication skills are equally important as analytical skills at Spotify where you collaborate across product, marketing, and executive teams.
Tips & Advice
Practice creating dashboards in Tableau and Power BI focusing on designing for specific use cases. Master data visualization principles: choose the right chart type for your data (line charts for trends over time, bar charts for comparisons, scatter plots for correlations, heatmaps for matrices). Use color strategically—highlight key insights, not every data point. Remove visual clutter. Make titles, axes, and labels crystal clear. Tell stories with data: start with the business question, show the data answering it, highlight the key insight, and end with the implication for the business. Adapt communication style for audience: executives get high-level conclusions and business impact; analysts need more depth and methodology; engineers want technical details. Practice presenting findings verbally—most often you'll explain dashboards in meetings. Prepare to discuss Spotify scenarios: 'How would you visualize user churn trends? Revenue by subscription tier? Artist popularity distribution?' Show you understand the goal is decision-making, not pretty charts. Ask about audience and decision needs before designing. Less is often more—a simple insight is more powerful than a complex dashboard.
Focus Topics
Tools and Platforms for Analysis and Visualization
Proficiency with Tableau and Power BI for creating interactive dashboards, Excel for rapid analysis and visualization, understanding tool capabilities and limitations, choosing appropriate tools for different analytical tasks
Practice Interview
Study Questions
Data Storytelling and Narrative Construction
Structuring findings into coherent narratives (business context → problem statement → finding → implication); leading with conclusions; supporting claims with data; connecting findings to strategy; making data memorable and emotionally resonant
Practice Interview
Study Questions
Audience-Appropriate Communication
Adapting communication style, technical depth, and messaging for different audiences (executives, product managers, engineers, customer support); knowing when to use metrics vs. anecdotes; adjusting visualization complexity; managing technical vs. business language
Practice Interview
Study Questions
Data Visualization Principles
Choosing appropriate chart types (line, bar, scatter, pie, heatmap) based on data and analytical question; using color, labels, and formatting effectively; designing visualizations that communicate insights clearly; following best practices for accessibility and clarity
Practice Interview
Study Questions
Dashboard and Report Design
Creating dashboards providing at-a-glance insights on key metrics, organizing information logically, selecting appropriate granularity and time periods, including context and benchmarks, enabling drill-down analysis for exploration
Practice Interview
Study Questions
Team Match and Behavioral - Onsite
What to Expect
This 60-minute onsite interview is conducted by a team member or hiring manager and assesses your fit with the team, collaboration style, and alignment with Spotify's culture and values. You'll be asked behavioral and situational questions about how you handle challenges, work with diverse colleagues, receive and give feedback, learn from mistakes, and approach problem-solving. The interviewer explores specific examples from your past work to evaluate qualities like bias-to-action (moving forward with imperfect information), resilience, learning agility, collaboration effectiveness, and passion for Spotify's mission around creator economy and fan engagement. This round is equally weighted to technical rounds—hiring managers want confidence they're adding someone who will thrive in the team environment.
Tips & Advice
Prepare 4-5 detailed examples using the STAR method (Situation, Task, Action, Result) covering: solving a complex problem with data, collaborating effectively across functions, receiving critical feedback and improving, advocating for a data-driven approach, and learning something new quickly under pressure. Use real situations with specific numbers/outcomes when possible, and emphasize what you learned. Research Spotify's values deeply and be ready discussing alignment—bias-to-action might mean shipping analysis with 80% certainty rather than waiting for perfect data. Ask thoughtful questions about team dynamics, how data influences decisions, and what success looks like in the first 90 days. Express genuine passion for Spotify's mission—why do you care about helping millions of creators and fans? Be authentic—interviewers detect insincerity. Discuss what feedback you actively seek and how you've implemented it. Show growth mindset: excitement about learning new tools, methodologies, or Spotify's business. Be honest about weaknesses, framing them as growth opportunities. Show you're evaluating team fit too—you're interviewing them as much as they're interviewing you. Demonstrate respect for diverse perspectives.
Focus Topics
Communication and Influence
How you influence others with data-backed arguments; explaining technical concepts to non-technical audiences; specific examples of changing someone's mind or perspective; handling respectful disagreement; presenting under pressure
Practice Interview
Study Questions
Bias-to-Action and Problem-Solving Initiative
Your comfort with ambiguity and making decisions with imperfect information; examples of identifying problems without explicit direction; taking ownership of solutions; balancing speed and accuracy in analysis
Practice Interview
Study Questions
Feedback Receptiveness and Continuous Learning
How you receive constructive criticism without defensiveness; specific examples of feedback changing your approach; learning from mistakes; commitment to skill development; staying current with new tools and methodologies
Practice Interview
Study Questions
Spotify Mission and Values Alignment
Your understanding of Spotify's mission to unlock human creativity by supporting millions of creators and billions of fans; passion for music, creators, or creative industries; examples from your background demonstrating mission alignment and creative/inclusive thinking
Practice Interview
Study Questions
Collaboration and Cross-Functional Teamwork
Your approach to working with product managers, engineers, designers, and other teams; how you communicate with non-technical stakeholders; examples of successful collaboration; conflict resolution and building consensus across different perspectives
Practice Interview
Study Questions
Frequently Asked Data Analyst Interview Questions
Sample Answer
Sample Answer
-- PostgreSQL example: returns NULL for campaigns with 0 impressions
SELECT
campaign_id,
SUM(conversions) AS total_conversions,
SUM(impressions) AS total_impressions,
CASE
WHEN SUM(impressions) = 0 THEN NULL
ELSE SUM(conversions)::numeric / SUM(impressions)::numeric
END AS weighted_rate
FROM events
GROUP BY campaign_id;SUM(conversions)::numeric / NULLIF(SUM(impressions), 0) AS weighted_rateSample Answer
Sample Answer
SELECT region,
SUM(o.amount) AS revenue,
COUNT(DISTINCT o.customer_id) AS active_customers,
SUM(o.amount)/NULLIF(COUNT(DISTINCT o.customer_id),0) AS rpc
FROM orders o
WHERE o.order_date >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY region;WITH visitors AS (
SELECT region, COUNT(DISTINCT user_id) AS visitors
FROM web_events
WHERE event_date >= CURRENT_DATE - INTERVAL '30 days' AND event_type='session'
GROUP BY region
),
purchasers AS (
SELECT region, COUNT(DISTINCT customer_id) AS purchasers
FROM orders
WHERE order_date >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY region
)
SELECT v.region,
v.visitors,
COALESCE(p.purchasers,0) AS purchasers,
COALESCE(p.purchasers,0)/NULLIF(v.visitors,0) AS conversion_rate
FROM visitors v
LEFT JOIN purchasers p USING(region);WITH c AS (
SELECT customer_id, region, COUNT(*) AS orders_cnt
FROM orders
WHERE order_date >= CURRENT_DATE - INTERVAL '180 days'
GROUP BY customer_id, region
)
SELECT region,
SUM(CASE WHEN orders_cnt>=2 THEN 1 ELSE 0 END)::float / COUNT(*) AS repeat_rate
FROM c
GROUP BY region;customers(id, acquired_via varchar, signup_date)
subscriptions(customer_id, start_date, price)
transactions(customer_id, amount, occurred_at)Sample Answer
WITH cust AS (
SELECT
id AS customer_id,
acquired_via AS channel,
signup_date,
signup_date + INTERVAL '12 months' AS signup_12m
FROM customers
),
-- revenue from transactions in the 12 months after signup
tx AS (
SELECT
c.customer_id,
c.channel,
SUM(t.amount) AS tx_revenue
FROM cust c
JOIN transactions t
ON t.customer_id = c.customer_id
AND t.occurred_at >= c.signup_date
AND t.occurred_at < c.signup_12m
GROUP BY c.customer_id, c.channel
),
-- revenue from subscriptions that start within 12 months (use price at start)
subs AS (
SELECT
c.customer_id,
c.channel,
SUM(s.price) AS subs_revenue
FROM cust c
JOIN subscriptions s
ON s.customer_id = c.customer_id
AND s.start_date >= c.signup_date
AND s.start_date < c.signup_12m
GROUP BY c.customer_id, c.channel
),
-- per-customer 12m revenue (coalesce to 0 when no rows)
cust_revenue AS (
SELECT
c.customer_id,
c.channel,
COALESCE(t.tx_revenue, 0) + COALESCE(s.subs_revenue, 0) AS revenue_12m
FROM cust c
LEFT JOIN tx t USING (customer_id, channel)
LEFT JOIN subs s USING (customer_id, channel)
),
-- per-channel aggregates: avg LTV and count of acquired customers in the period
channel_customers AS (
SELECT
channel,
COUNT(*) AS num_customers,
AVG(revenue_12m) AS avg_ltv,
SUM(revenue_12m) AS total_revenue
FROM cust_revenue
GROUP BY channel
),
-- marketing spend aggregated by channel over months corresponding to customer signups.
-- We map signup_date to month for attribution; adjust as needed.
channel_spend AS (
SELECT
m.channel,
SUM(m.amount) AS total_spend
FROM marketing_spend m
-- restrict spend to months where there were signups (optional)
JOIN (
SELECT DISTINCT channel, DATE_TRUNC('month', signup_date) AS month
FROM customers
) cs ON cs.channel = m.channel AND cs.month = m.month
GROUP BY m.channel
)
-- final: combine LTV and CAC
SELECT
cc.channel,
cc.num_customers,
cc.avg_ltv,
cc.total_revenue,
COALESCE(cs.total_spend, 0) AS total_spend,
CASE WHEN cc.num_customers > 0 THEN COALESCE(cs.total_spend,0)::numeric / cc.num_customers ELSE NULL END AS avg_cac
FROM channel_customers cc
LEFT JOIN channel_spend cs USING (channel)
ORDER BY cc.total_revenue DESC;Sample Answer
Sample Answer
Sample Answer
-- identify revenue rows present in Source but absent in Mart
SELECT s.order_id FROM source_revenue s
LEFT JOIN mart_revenue m ON s.order_id = m.order_id
WHERE m.order_id IS NULL AND s.month = '2025-10';Sample Answer
Sample Answer
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