Amazon Senior Data Analyst Interview Preparation Guide
Amazon's Senior Data Analyst interview process is comprehensive and multi-staged, designed to assess technical depth, business acumen, and alignment with Amazon's Leadership Principles. The interview loop includes an initial recruiter screening, followed by a technical assessment, and multiple onsite rounds covering SQL proficiency, data case studies, advanced analytics, behavioral competencies, and manager alignment. Candidates are evaluated on their ability to write optimized SQL queries, structure complex business problems, translate data insights into actionable recommendations, and demonstrate ownership and customer obsession in their work.
Interview Rounds
Recruiter Screening
What to Expect
The initial recruiter call lasts approximately 30-45 minutes and serves as the first evaluation stage. The recruiter will explore your background, core analytics skills, and overall cultural fit for the role. You'll discuss your hands-on experience with SQL, Excel, and BI tools like Tableau or QuickSight. The recruiter will also assess your familiarity with Amazon's business model, why you're interested in the company, and how you align with Amazon's Leadership Principles such as Ownership and Deliver Results. This round also covers your experience collaborating with cross-functional teams—engineering, product, business stakeholders—and how you've influenced decisions through data-driven insights. Use this opportunity to demonstrate enthusiasm for solving data problems at scale and your understanding of how analytics drive business outcomes at Amazon.
Tips & Advice
Keep examples concise but quantifiable; mention specific metrics and business outcomes you've influenced (e.g., 'improved report efficiency by 25% by automating X process'). Numbers make your experience credible and memorable. Research Amazon's business model and recent announcements to show genuine interest beyond compensation. Prepare a 30-second elevator pitch about why you want to work at Amazon specifically, not just why you want a data analyst role. Connect your past work to Amazon's principles and scale. Ask thoughtful questions about the team structure and data infrastructure to show you've done your homework.
Focus Topics
Motivation and Interest in Amazon
Articulate why you specifically want to join Amazon's data organization. Reference company announcements, technology stack, scale of problems, or specific teams if possible.
Practice Interview
Study Questions
Cross-Functional Collaboration Experience
Describe specific examples of working with product, engineering, finance, or operations teams. Highlight how you've influenced non-technical stakeholders through data insights and adapted communication for different audiences.
Practice Interview
Study Questions
Technical Tool Proficiency
Demonstrate hands-on experience with SQL, Excel (advanced functions like pivot tables, VLOOKUP, INDEX-MATCH), and BI tools such as Tableau or QuickSight. Be prepared to discuss which tools you prefer for different scenarios.
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Study Questions
Background and Experience Storytelling
Articulate your analytics journey, key projects, and quantifiable outcomes. Highlight progression toward senior-level responsibilities and increasing scope of impact.
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Amazon Leadership Principles Alignment
Be familiar with Amazon's 16 Leadership Principles, particularly Ownership, Deliver Results, Dive Deep, and Bias for Action. Prepare examples demonstrating how you embody these in your work.
Practice Interview
Study Questions
Technical Assessment - SQL and Problem Solving
What to Expect
This online assessment round evaluates your technical fundamentals under time pressure and typically lasts 60-90 minutes. You'll encounter 3-5 SQL problems ranging from basic to intermediate-advanced complexity, logic puzzles, and data interpretation questions. The SQL portion tests your ability to write queries that join multiple tables, perform aggregations with GROUP BY and HAVING clauses, filter data efficiently, and use window functions. You may also face questions about query optimization and understanding execution plans. Logic puzzles assess your analytical thinking and ability to approach problems systematically. Data interpretation questions require you to analyze datasets or dashboards and draw meaningful conclusions. This round often uses platforms like HackerRank, LeetCode, or Amazon's internal CollaEdit tool. For senior level, expect more complex scenarios and emphasis on efficiency.
Tips & Advice
Read each SQL problem carefully before diving into code. Start with the most straightforward solution, then optimize for readability and performance. Test your queries mentally against edge cases (nulls, duplicates, empty results). For window functions, ensure you understand the PARTITION BY and ORDER BY clauses. In logic puzzles, think out loud and explain your reasoning—interviewers value your approach. Manage time; if stuck on a problem for more than 10 minutes, move forward and return if time permits. At senior level, discuss trade-offs between query complexity, maintainability, and performance. Avoid overly clever or obscure SQL syntax; clarity is valued at Amazon.
Focus Topics
Logic Puzzles and Analytical Problem-Solving
Approach puzzles systematically by breaking them into smaller parts. Use examples and test cases to validate your logic. Practice on platforms like LeetCode's database section and classic logic puzzle collections.
Practice Interview
Study Questions
Data Interpretation and Pattern Recognition
Given raw data or a dataset, identify trends, outliers, and patterns. Write brief explanations of findings. Answer questions about what data means in business context.
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Study Questions
SQL Performance and Query Optimization
Understand indexes, query execution plans, and optimization strategies. Minimize the use of subqueries where joins suffice. Recognize when to use CTEs (Common Table Expressions) for readability. Identify inefficient patterns and suggest improvements.
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Study Questions
Window Functions and Aggregations
Understand ROW_NUMBER(), RANK(), DENSE_RANK(), LAG(), LEAD(), and aggregate window functions like SUM() OVER(). Use PARTITION BY and ORDER BY correctly. Combine window functions with filtering for complex calculations.
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Study Questions
SQL Joins and Complex Query Construction
Master INNER, LEFT, RIGHT, and FULL OUTER joins. Practice multi-table joins (3+ tables), self-joins for hierarchical data, and understanding join order impact. Handle NULL values correctly in join conditions.
Practice Interview
Study Questions
SQL Technical Interview
What to Expect
This onsite interview round focuses intensively on SQL proficiency and typically lasts 45-60 minutes. You'll be asked to write and optimize real-world SQL queries, often in a shared editor like CollaEdit where your interviewer can see your work in real-time. Expect scenarios based on Amazon's actual datasets or realistic analogs—queries involving customer orders, product inventory, marketplace transactions, or supply chain metrics. You'll need to demonstrate not just correctness but also optimization skills: selecting appropriate join types, using window functions efficiently, understanding query execution plans, and considering schema design implications. The interviewer may ask follow-up questions like 'How would you improve this query?' or 'What would happen if the dataset scaled to billions of rows?' At senior level, you're expected to explain your reasoning step-by-step, discuss trade-offs between readability and performance, and suggest alternative approaches.
Tips & Advice
Explain your thought process aloud as you code—interviewers value reasoning as much as correctness. Start with a clear understanding of what the query should return, then build incrementally. Test your logic on sample data before declaring it complete. When optimizing, discuss the trade-offs: a simpler query might be more readable but slower; a complex one might be efficient but harder to maintain. Use proper aliasing and formatting for readability. At senior level, ask clarifying questions about data volume, update frequency, and business requirements—this shows maturity. If you hit a blocker, think out loud about alternatives rather than sitting silently. Finish by discussing how your solution scales and what assumptions you've made.
Focus Topics
Handling Edge Cases and Data Quality Issues
Account for NULL values, duplicates, and missing data in queries. Validate results for correctness. Discuss data quality issues and how to handle them (e.g., duplicate customer IDs, incomplete records).
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Study Questions
Schema Design and Data Modeling Implications
Understand normalization vs. denormalization trade-offs. Discuss how schema design impacts query patterns. Suggest schema improvements for better query performance or analytical flexibility.
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Window Functions for Advanced Analytics
Use PARTITION BY and ORDER BY to compute running totals, rankings, percentiles, and moving averages. Combine with WHERE clauses and filtering. Solve complex analytical problems elegantly with window functions.
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Study Questions
Query Optimization and Execution Plans
Understand how to read query execution plans, identify bottlenecks (full table scans, expensive sorts), and optimize accordingly. Know when to use indexes, materialized views, or denormalization. Consider I/O costs and data volume.
Practice Interview
Study Questions
Complex Multi-Table SQL Queries (Redshift/PostgreSQL)
Write queries joining 4+ tables with proper filtering, aggregation, and ordering. Understand Redshift-specific syntax and optimization techniques. Handle complex WHERE clauses and conditional logic.
Practice Interview
Study Questions
Data Case Study and Business Analysis Interview
What to Expect
This onsite interview round lasts approximately 60 minutes and tests your ability to structure ambiguous business problems and derive actionable insights from data. You'll be presented with realistic scenarios such as analyzing customer churn trends, optimizing supply chain metrics, evaluating marketing campaign effectiveness, or diagnosing a dip in marketplace transactions. You're expected to ask clarifying questions, define success metrics independently, hypothesize root causes, and propose data-driven recommendations. The interviewer will provide datasets, dashboards, or raw metrics and expects you to navigate ambiguity, prioritize analysis steps, and communicate findings clearly. At senior level, you must demonstrate strategic thinking: connecting data insights to business outcomes, considering cross-functional impacts, and suggesting follow-up analyses. This round emphasizes problem-solving rigor and business acumen as much as technical execution.
Tips & Advice
Start by clarifying the problem statement with the interviewer. Define what success looks like and establish key metrics before diving into data. Use a logical framework: Problem Definition → Data Exploration → Hypothesis Formation → Root Cause Analysis → Recommendations → Business Impact. Draw on sample data provided and think aloud about patterns and anomalies. Avoid jumping to conclusions; instead, iteratively narrow your focus. At senior level, consider second-order effects: How does this recommendation affect other teams? What are trade-offs? Propose follow-up analyses or experiments. Use rough calculations and estimates when precise numbers aren't available—order-of-magnitude thinking is valued. End with a clear action plan: 'I recommend we do X because it will improve Y by Z%.' Connect your analysis to Amazon's business strategy and customer impact.
Focus Topics
Cross-Functional Impact and Stakeholder Considerations
Think beyond your immediate domain. Consider how recommendations affect product, engineering, finance, and operations. Anticipate objections and align across teams.
Practice Interview
Study Questions
Root Cause Analysis and Hypothesis Testing
Develop testable hypotheses for observed patterns. Break down problems into components. Use data to validate or refute hypotheses. Distinguish correlation from causation. Consider multiple potential causes.
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Study Questions
Business Metrics Definition and KPI Selection
Define appropriate metrics for the problem (e.g., conversion rate, churn, lifetime value, cost per acquisition). Understand leading vs. lagging indicators. Explain why chosen metrics matter for the business.
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Translating Data Insights into Business Recommendations
Convert analytical findings into actionable, business-focused recommendations. Quantify potential impact. Consider feasibility, timeline, and resource requirements. Present trade-offs and alternatives.
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Problem Framing and Scoping
Take an ambiguous business challenge and clearly define the problem, scope, and success metrics. Ask clarifying questions about stakeholders, timeline, and constraints. Prioritize analysis areas when resources are limited.
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Study Questions
Advanced Analytics and Metrics Deep-Dive Interview
What to Expect
This onsite interview round, lasting approximately 60 minutes, assesses deeper analytical and technical expertise. You'll discuss advanced topics such as statistical analysis, experimental design, building and maintaining complex metrics, dashboard design principles, and data pipeline understanding. The interviewer may present scenarios like: designing an A/B test for a new feature, understanding why a critical metric dropped, or building an automated reporting system. At senior level, you're expected to demonstrate knowledge of statistical concepts (hypothesis testing, confidence intervals, p-values), understand trade-offs in metric design, and explain how data flows through systems. You should be able to discuss when to use certain analytical approaches and their limitations. This round differentiates senior analysts from mid-level by testing strategic analytics thinking, not just execution.
Tips & Advice
Be prepared to discuss statistical concepts in plain language—avoid jargon unless you explain it clearly. When asked about metrics, explain why you'd choose one approach over another and discuss edge cases. For experimental design, mention randomization, sample size, and duration considerations. Discuss how you'd communicate results to non-technical stakeholders. At senior level, show awareness of common pitfalls: survivorship bias, Simpson's paradox, p-hacking. Ask about data quality and infrastructure constraints when designing solutions. If unsure about something, say so and explain how you'd learn or investigate. Connect everything back to business outcomes and customer impact.
Focus Topics
Data Pipelines and ETL Understanding
Understand how data flows from sources to warehouses. Know basics of ETL processes, data validation, and pipeline reliability. Discuss data quality checks and how to handle failures.
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Study Questions
Dashboard Design and Data Visualization Best Practices
Design dashboards that answer specific business questions. Choose appropriate visualizations (charts, tables, heatmaps). Optimize for clarity and usability. Understand audience and their information needs.
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Statistical Analysis and Hypothesis Testing
Understand p-values, confidence intervals, and statistical significance. Know when to use t-tests, chi-square tests, or other statistical methods. Discuss Type I and Type II errors. Explain results to non-technical audiences.
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Study Questions
Experimental Design and A/B Testing
Design valid A/B tests: randomization, sample size calculation, treatment duration, and success metrics. Understand confounding variables and how to control for them. Discuss when not to run experiments.
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Metrics Design and KPI Governance
Build robust metrics that accurately represent business goals. Design dashboards with appropriate metrics, drill-downs, and alerts. Understand metric calculation methodologies and potential pitfalls. Maintain metric definitions across teams.
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Study Questions
Leadership Principles and Behavioral Interview
What to Expect
This onsite behavioral interview lasts approximately 45-60 minutes and evaluates how you embody Amazon's Leadership Principles through real examples. Interviewers will ask questions such as: 'Tell me about a time you took ownership of a project,' 'Describe a situation when you made a data-driven decision that didn't go as planned,' 'Give an example of diving deep to understand a complex problem,' or 'How have you influenced others without direct authority?' You'll be assessed on your ability to connect specific experiences to Amazon principles like Ownership, Deliver Results, Dive Deep, and Bias for Action. At senior level, interviewers also explore leadership capabilities: mentoring junior team members, influencing cross-functional decisions, managing ambiguity, and driving change. Use the STAR format (Situation, Task, Action, Result) and always quantify outcomes. This round is equally important as technical rounds at Amazon; many candidates fail here despite strong technical skills.
Tips & Advice
Prepare 5-8 concrete, specific examples from your career that demonstrate different Amazon Leadership Principles. Use the STAR format consistently: Situation (context), Task (your role/responsibility), Action (what you specifically did, not the team), Result (quantified outcome). Focus on 'I' not 'we'—demonstrate your personal contribution. For each example, identify which principle(s) it illustrates. At senior level, prepare examples showing: mentorship and developing others, influencing without authority, owning large-scope projects, learning from failure, and driving decisions with incomplete information. Interviewers often probe deeper—be ready to answer follow-ups: 'What would you do differently?' 'How did you measure success?' Practice speaking concisely; aim for 1-2 minute answers. Avoid overly polished stories; authenticity matters more than perfection. If an interviewer interrupts, accept gracefully—they're gathering specific information.
Focus Topics
Amazon Leadership Principle: Dive Deep
Share examples of understanding complex problems at a fundamental level, asking probing questions, and not accepting surface-level explanations. Show curiosity and rigor in analysis.
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Study Questions
Handling Failure and Learning from Setbacks
Describe a specific instance where an analysis or project didn't go as planned. Explain what you learned, how you communicated it, and what you changed as a result. Show growth mindset.
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Other Amazon Leadership Principles (Customer Obsession, Bias for Action, Learn and Be Curious, Frugality, etc.)
Prepare examples for other principles relevant to your experience. Be ready to discuss how you've embodied 3-4 different principles across various situations.
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Study Questions
Amazon Leadership Principle: Ownership
Demonstrate taking full responsibility for outcomes, even beyond your formal role. Show long-term thinking and persistence in solving problems. Examples: owning a critical project end-to-end, fixing a systemic issue without being asked, or driving change in team processes.
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Study Questions
Senior-Level Leadership: Mentoring and Developing Others
At senior level, provide examples of mentoring junior analysts, elevating team capabilities, and creating systems for others' success. Show how you've grown people and expanded team capacity.
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Study Questions
Amazon Leadership Principle: Deliver Results
Provide examples of meeting commitments despite obstacles, solving problems under pressure, and achieving measurable outcomes. Show bias toward action and follow-through.
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Study Questions
Manager Round Interview
What to Expect
This final onsite interview round, lasting approximately 45 minutes, is with your prospective manager or a senior leader on the team. The focus shifts from rigorous technical and behavioral evaluation to team fit, working style alignment, and mutual assessment. The manager will explore how you collaborate, communicate, handle feedback, and align with team goals. Questions might include: 'Tell me about your working style,' 'How do you prefer to receive feedback?' 'What kind of projects interest you most?' 'How would you approach a conflict with a cross-functional partner?' You'll also have opportunity to ask detailed questions about the team, products, infrastructure, and growth opportunities. At senior level, the manager is also assessing whether you can take ownership of strategic projects, mentor junior team members, and drive team-level improvements. This round is less about gotcha questions and more about ensuring a good mutual fit before an offer.
Tips & Advice
Research the team and manager beforehand if possible—understand their recent launches, team size, and reported focus areas. Be authentic and natural in this conversation; managers value genuine fit over rehearsed answers. Prepare thoughtful questions that show you've considered the role seriously: 'What does success look like for this role in the first year?' 'How is the team organized?' 'What are current technical priorities?' 'How does this role evolve?' At senior level, ask about mentorship opportunities, ownership scope, and strategic projects. Be honest about your working style, communication preferences, and growth goals. If the manager raises concerns, address them directly and respectfully. This is a two-way evaluation—you're assessing whether you want to work there too. Express genuine enthusiasm for the opportunity while demonstrating that you're selective about fits.
Focus Topics
Clarifying Role Expectations and Fit
Ask substantive questions about success metrics for the role, team dynamics, current challenges, and the data landscape. Demonstrate you've researched and thought about what success looks like.
Practice Interview
Study Questions
Learning and Growth Mindset
Express genuine curiosity about new tools, techniques, and domains. Show willingness to learn from teammates and from failures. Articulate your growth goals and how this role supports them.
Practice Interview
Study Questions
Communication and Collaboration Style
Describe how you prefer to communicate (synchronous vs. async, detailed vs. high-level), handle ambiguity in requirements, and work effectively with teams. Show flexibility and openness to different working styles.
Practice Interview
Study Questions
Senior-Level: Strategic Thinking and Team Impact
Discuss how you think about team-level improvements, process enhancements, or capability building beyond individual projects. Show interest in understanding broader business strategy.
Practice Interview
Study Questions
Ownership and Autonomy
Discuss how you take ownership of projects, seek feedback when needed, and work independently. Show balance between seeking guidance and driving decisions. At senior level, explain how you'd enable others.
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Study Questions
Frequently Asked Data Analyst Interview Questions
Sample Answer
Sample Answer
WITH cohorts AS (
-- cohort_week = Monday of the signup week
SELECT
user_id,
DATE_TRUNC(signup_date, WEEK) AS cohort_week,
signup_date
FROM users
),
user_events AS (
-- keep only events within 4 weeks of signup and compute week offset
SELECT
c.user_id,
c.cohort_week,
e.event_date,
FLOOR(DATE_DIFF(e.event_date, c.signup_date, DAY) / 7) AS week_offset
FROM cohorts c
JOIN events e
ON e.user_id = c.user_id
WHERE e.event_date >= c.signup_date
AND DATE_DIFF(e.event_date, c.signup_date, DAY) BETWEEN 0 AND 27
),
user_week_flags AS (
-- dedupe so each user is counted at most once per week offset
SELECT DISTINCT
user_id,
cohort_week,
week_offset
FROM user_events
),
cohort_sizes AS (
-- total new users per cohort (week0 population)
SELECT
cohort_week,
COUNT(*) AS cohort_users
FROM cohorts
GROUP BY cohort_week
),
retention_counts AS (
-- count unique users per cohort per week offset
SELECT
cohort_week,
week_offset,
COUNT(*) AS users_retained
FROM user_week_flags
GROUP BY cohort_week, week_offset
),
retention_pivot AS (
SELECT
cs.cohort_week,
cs.cohort_users,
COALESCE(rc0.users_retained,0) AS week0,
COALESCE(rc1.users_retained,0) AS week1,
COALESCE(rc2.users_retained,0) AS week2,
COALESCE(rc3.users_retained,0) AS week3
FROM cohort_sizes cs
LEFT JOIN retention_counts rc0 ON cs.cohort_week = rc0.cohort_week AND rc0.week_offset = 0
LEFT JOIN retention_counts rc1 ON cs.cohort_week = rc1.cohort_week AND rc1.week_offset = 1
LEFT JOIN retention_counts rc2 ON cs.cohort_week = rc2.cohort_week AND rc2.week_offset = 2
LEFT JOIN retention_counts rc3 ON cs.cohort_week = rc3.cohort_week AND rc3.week_offset = 3
)
SELECT
cohort_week,
cohort_users,
ROUND(100.0 * week0 / NULLIF(cohort_users,0),2) AS week0_pct,
ROUND(100.0 * week1 / NULLIF(cohort_users,0),2) AS week1_pct,
ROUND(100.0 * week2 / NULLIF(cohort_users,0),2) AS week2_pct,
ROUND(100.0 * week3 / NULLIF(cohort_users,0),2) AS week3_pct
FROM retention_pivot
ORDER BY cohort_week;Sample Answer
Sample Answer
Sample Answer
Sample Answer
Sample Answer
-- returns missing columns (empty => pass)
WITH expected AS (SELECT column_name FROM (VALUES
('id'),('user_id'),('event_time'),('amount')) AS t(column_name))
SELECT e.column_name
FROM expected e
LEFT JOIN information_schema.columns c
ON c.table_schema='staging' AND c.table_name='my_table' AND c.column_name = e.column_name
WHERE c.column_name IS NULL;SELECT column_name, data_type
FROM information_schema.columns
WHERE table_schema='staging' AND table_name='my_table'
AND (column_name, data_type) IN (('amount','numeric'),('event_time','timestamp without time zone'));
-- CI compares returned types to expected mapping-- fail if null rate > 1%
SELECT SUM(CASE WHEN amount IS NULL THEN 1 ELSE 0 END)::float / COUNT(*) AS null_rate
FROM staging.my_table
HAVING (SUM(CASE WHEN amount IS NULL THEN 1 ELSE 0 END)::float / COUNT(*)) > 0.01;-- check user_id in expected domain (e.g., positive ints) and sample unexpected values
SELECT user_id, COUNT(*) as cnt
FROM staging.my_table
WHERE user_id <= 0 OR user_id IS NULL
GROUP BY user_id
LIMIT 20;WITH bad AS (
SELECT COUNT(*) AS bad_count FROM staging.my_table WHERE status NOT IN ('new','open','closed')
), tot AS (
SELECT COUNT(*) AS total FROM staging.my_table
)
SELECT bad_count, total FROM bad, tot
WHERE bad_count::float / NULLIF(total,0) > 0.001; -- fail if >0.1% unexpectedSample Answer
Sample Answer
Sample Answer
WITH base_events AS (
-- normalized events with user_id and event_date
SELECT user_id, CAST(event_ts AS date) AS event_date
FROM events
WHERE event_ts >= '2023-01-01'
),
monthly_users AS (
-- flag unique users per month
SELECT
DATE_TRUNC('month', event_date) AS month,
user_id
FROM base_events
GROUP BY 1, 2
),
mau AS (
-- count unique users per month
SELECT
month,
COUNT(*) AS mau
FROM monthly_users
GROUP BY month
ORDER BY month
)
SELECT
month,
mau,
-- 3-month moving average including current month
ROUND(AVG(mau) OVER (ORDER BY month ROWS BETWEEN 2 PRECEDING AND CURRENT ROW), 2) AS mau_3mo_avg
FROM mau;Search Results
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