Amazon Business Intelligence Analyst Interview Preparation Guide - Senior Level
Amazon's Business Intelligence Engineer interview process for senior-level candidates consists of 7 total rounds spanning approximately 4-6 weeks from initial contact to offer decision. The process begins with a recruiter screening call, followed by a technical phone screen evaluating SQL and Python proficiency, and concludes with 5 onsite interviews conducted back-to-back in a single day or split across 1-2 days. Each round evaluates specific competencies aligned with Amazon's 14 Leadership Principles, with particular emphasis on customer obsession, ownership, diving deep, and delivering results. Senior candidates are expected to demonstrate not only technical mastery but also strategic business thinking, leadership maturity, and the ability to influence organizational decisions through data-driven insights.[1][2][3]
Interview Rounds
Recruiter Screening
What to Expect
The initial phone screen conducted by an Amazon recruiter lasts approximately 30 minutes and focuses on understanding your background, career motivations, and cultural fit with Amazon. The recruiter will verify your interest in the role, confirm your experience level, assess whether your background aligns with the position requirements, and outline the interview process. This round is also your opportunity to ask questions about the team, role expectations, and how business intelligence functions within Amazon's organization. You should be prepared to discuss your most significant achievements and why you're interested in this specific role at Amazon. For senior candidates, recruiters will also probe your experience with team leadership, cross-functional collaboration, and scope of technical projects you've owned.[1]
Tips & Advice
Be enthusiastic but genuine about Amazon and this specific role. Research the team or department you're interviewing for and reference specific initiatives, products, or business challenges they've worked on. Prepare a concise 2-3 minute overview of your career progression, highlighting your trajectory toward senior-level responsibilities and key impact milestones. Ask thoughtful questions about team structure, current analytical priorities, and the interviewer's experience at Amazon. Listen carefully to the specific business context and problems the team is solving, and address how your experience maps to those challenges. For senior candidates, mention experience with mentoring, process improvements, or initiatives where you've improved data quality or analytics infrastructure. Confirm understanding of the interview timeline, what to expect in subsequent rounds, and the team structure you'd be joining.
Focus Topics
Experience with BI Tools and Data Technologies
Overview of your hands-on experience with BI platforms (Tableau, Power BI, QuickSight), SQL databases, ETL tools, and data infrastructure. Mention production systems you've built and scale of data you've worked with.
Practice Interview
Study Questions
Most Proud Achievement with Business Context
A specific project or accomplishment where you demonstrated measurable impact, owned the outcome end-to-end, solved a complex problem, and grew professionally. Should highlight business value delivered, technical challenges overcome, and how the work aligned with organizational strategy.
Practice Interview
Study Questions
Motivation and Role Alignment with Amazon's Scale and Culture
Clear reasons why you're interested in Amazon specifically, this role, and how your experience matches the job description. Connect your technical expertise and business acumen to Amazon's scale, customer obsession, and strategic priorities.
Practice Interview
Study Questions
Career Narrative and Senior-Level Progression
Clear articulation of your career journey from junior to senior levels, key roles held, increasing scope of responsibilities, and measurable impact delivered. Include examples of projects you've owned end-to-end and growth in leadership or mentorship responsibilities.
Practice Interview
Study Questions
Technical Phone Screen - SQL and Data Manipulation
What to Expect
A 60-minute technical phone screen where you'll solve 1-2 SQL problems on a shared code editor (typically HackerRank, LeetCode, or similar platform). You'll be given realistic Amazon schemas (e.g., orders, customers, transactions, sessions, fulfillment data) and asked to write queries to answer business questions.[1] The interviewer will assess your ability to write clean, correct SQL, optimize queries for large datasets, articulate your reasoning, and handle edge cases. For senior candidates, expect questions involving window functions, CTEs (Common Table Expressions), complex aggregations, query optimization, and understanding performance implications at scale. You may also be asked brief questions about Python for data manipulation, data validation approaches, or how you'd approach a larger analytics problem.[2]
Tips & Advice
Before writing code, ask clarifying questions about the schema, expected data volume, business context, and what the results will be used for. For senior candidates, verbally explain your approach, including which joins you'll use and why, before diving into coding. Write clean, readable SQL with proper table aliasing and formatting—senior-level code should be maintainable. Test your queries mentally against edge cases (NULL handling, duplicates, empty result sets, data type mismatches). When optimizing, discuss indexing strategies, query execution plans, partitioning approaches, and table statistics rather than just rewriting syntax.[3] If stuck, think out loud—interviewers value problem-solving approach over perfect solutions. Practice on DataLemur or LeetCode specifically for medium-to-hard SQL problems focused on real-world analytics scenarios with business context.
Focus Topics
Real-World Data Scenarios and Data Quality Handling
Handling NULL values appropriately, managing duplicate records, data type conversions, time-based calculations with timezone awareness. Cohort analysis, user retention, funnel analysis, and generating reports with proper validation.
Practice Interview
Study Questions
Explaining Technical Decisions and Trade-Offs
Clear communication of your approach, trade-offs considered, and reasoning for specific SQL techniques chosen. Discussing alternative approaches and why you selected one. Mentioning assumptions about data or performance constraints.
Practice Interview
Study Questions
CTEs and Complex Join Strategies
Writing Common Table Expressions (CTEs) for readability and intermediate calculations. Understanding INNER, LEFT, RIGHT, FULL OUTER, and CROSS joins and when each is appropriate. Self-joins, multiple join levels, and handling join order for query optimization.
Practice Interview
Study Questions
Query Optimization and Performance Analysis
Identifying bottlenecks in queries through execution plans and statistics. Understanding indexing strategies, avoiding full table scans on large datasets, partitioning benefits, filter order optimization. Discussing when to use subqueries vs. joins, materialized views vs. computed on-the-fly.
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Study Questions
Complex SQL Queries with Window Functions
Advanced queries using ROW_NUMBER(), RANK(), DENSE_RANK(), LAG(), LEAD(), and aggregate window functions. Problems involving running totals, moving averages, year-over-year comparisons, cohort analysis, and cumulative metrics. Understanding frame specifications (ROWS BETWEEN, PARTITION BY with multiple columns).
Practice Interview
Study Questions
Onsite Round 1 - Data Modeling and ETL Architecture
What to Expect
A 60-minute onsite round where you'll work with a senior BI engineer, data engineer, or technical lead on a data modeling and ETL design problem. You'll be given a realistic business scenario (e.g., 'Design a data warehouse schema for Amazon's e-commerce order analytics' or 'Build a real-time pipeline to track fulfillment metrics') and asked to design the underlying data structure, ETL processes, and operational considerations.[2] The interviewer will explore your understanding of dimensional modeling, fact and dimension tables, slowly changing dimensions (SCD), data validation, automated testing, and scalability considerations. For senior candidates, expect deep discussion of production considerations, data quality mechanisms, handling edge cases, and trade-offs between batch and real-time approaches.
Tips & Advice
Start by clarifying business requirements: What are the key metrics? Who are the end users? What's the data volume and refresh frequency? How fresh does data need to be? Sketch your data model on the whiteboard or shared document, clearly labeling fact and dimension tables, primary/foreign keys, data types, and relationships. Explain your choice of slowly changing dimension type (Type 1, 2, or 3) based on business needs and query patterns. Discuss data validation steps and how you'd detect anomalies in the pipeline.[3] For senior level, discuss how the pipeline scales to handle billions of events, redundancy and disaster recovery mechanisms, cost optimization, and monitoring/alerting for data quality issues. Consider bringing up compliance requirements, data retention policies, security, and governance. Show that you've thought about operational concerns like pipeline restartability and idempotency.
Focus Topics
Scalability, Cost Optimization, and Production Readiness
Designing for large-scale data ingestion, partitioning strategies for performance and cost, handling late-arriving or out-of-order data, ensuring system resilience and redundancy. Operational monitoring, alerting, and runbook documentation.
Practice Interview
Study Questions
Data Validation, Quality Checks, and Anomaly Detection
Designing automated validation in ETL pipelines to detect duplicates, NULL anomalies, data type mismatches, and statistical outliers. Implementing alerting and recovery mechanisms. Reconciliation between source and target systems.
Practice Interview
Study Questions
Slowly Changing Dimension (SCD) Strategies
Understanding and implementing Type 1 (overwrite), Type 2 (track history with effective dates), Type 3 (limited history), and hybrid approaches. Choosing the right type for each dimension based on business requirements, query patterns, and storage trade-offs.
Practice Interview
Study Questions
Dimensional Modeling and Star Schema Design
Designing fact and dimension tables optimized for analytical workloads. Understanding conformed dimensions, reusable dimensions across multiple fact tables, and grain (level of detail) at different aggregation levels. Designing for both query performance and analytical flexibility.
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Study Questions
ETL Pipeline Architecture and Data Flow Design
Designing end-to-end data pipelines from source systems through transformation to warehouse. Batch vs. streaming trade-offs, incremental vs. full-load approaches, error handling, failure recovery, idempotency, and data reconciliation processes.
Practice Interview
Study Questions
Onsite Round 2 - Advanced SQL and Business Analytics
What to Expect
A 60-minute onsite technical round focused on solving complex analytical SQL problems related to business metrics, KPI calculations, and data-driven insights.[1][3] You'll receive a realistic Amazon business scenario (e.g., 'Calculate customer retention cohorts across regions', 'Identify product categories with declining sales and diagnose the cause', 'Analyze A/B test results and provide statistical conclusions') and write SQL to extract insights and answer business questions. The emphasis is on translating ambiguous business questions into clear SQL logic, handling complex requirements, validating results, and explaining business context. Senior candidates should demonstrate ability to think holistically about metrics, consider data quality implications, and discuss how insights would be communicated to and actioned by stakeholders.
Tips & Advice
Before writing SQL, restate the business question in your own words and clarify any ambiguities with the interviewer. Discuss your approach at a high level first—what's your analytical strategy? For senior candidates, think about edge cases, data quality considerations, how you'd validate results, and potential biases in the data. Write clear, modular SQL that other analysts could understand and maintain without extensive documentation. If the problem involves complex calculations, break it into CTEs so your logic is transparent and each step is testable. Discuss any assumptions about data definitions, time windows, or filters that could affect results. For senior level, mention how you'd measure impact of findings, what validation you'd perform, how you'd communicate findings to drive action, and what guardrails or context non-technical stakeholders need.
Focus Topics
A/B Testing Analysis and Statistical Validation
Calculating test metrics, uplift, statistical significance, confidence intervals, and practical significance. Comparing control and treatment groups correctly, detecting anomalies or data issues in test results. Understanding when results are actionable vs. inconclusive.
Practice Interview
Study Questions
Trend Analysis, Anomaly Detection, and Diagnostic Analytics
Detecting trends in time-series metrics using window functions and time-based aggregations. Identifying unusual patterns, breakpoints, or anomalies in data that warrant investigation. Systematic segmentation to diagnose root causes of metric movements.
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Study Questions
Data Validation, Reconciliation, and Result Integrity
Validating analytical results against source systems and other dashboards, identifying and resolving discrepancies. Documenting assumptions, filters, and business logic. Ensuring reproducibility and auditability of analyses.
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Study Questions
KPI Definition, Calculation, and Metric Consistency
Translating business requirements into precise, unambiguous metric definitions. Calculating metrics like conversion rates, daily active users, average order value, retention rates with proper accounting for edge cases and business logic. Ensuring metric definitions are reproducible and consistent across reporting systems.
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Study Questions
Cohort Analysis and Customer Lifecycle Metrics
Building cohort tables based on acquisition date or user action, calculating retention rates across time periods, churn analysis, and lifetime value. Using window functions for efficient cohort-based analysis. Handling different retention definitions (active on day X, purchased in time window, etc.).
Practice Interview
Study Questions
Onsite Round 3 - Metrics, Product Sense, and Strategic Analytics
What to Expect
A 60-minute round with a senior BI engineer, product manager, or senior data scientist focused on your ability to translate vague business questions into clear metrics, frame problems analytically, and drive data-informed decisions.[1] You may be given an open-ended business challenge (e.g., 'How would you evaluate if a new feature is successful?', 'We've noticed a 15% dip in conversions—how would you diagnose the root cause and what would you recommend?', 'How should we price a new product category to maximize revenue?') and asked to define success metrics, identify key dimensions to analyze, and propose analytical approaches. This round evaluates your product sense, business acumen, strategic thinking, and ability to navigate ambiguity. For senior candidates, expect discussion of trade-offs between competing metrics, how you'd prioritize analytical work, and how you'd present findings to drive executive-level decisions.
Tips & Advice
Start by asking clarifying questions: What's the business context and strategic importance? What decisions does this metric inform? Who's the audience (operations team, product, executives)? What data is available and what are data latency constraints? For senior candidates, demonstrate strategic thinking by discussing which metrics matter most to the business and why. Propose multiple analytical approaches and discuss trade-offs (speed vs. precision, leading vs. lagging indicators, sampling vs. population analysis). Use a framework like breaking down metrics by customer segment, geography, product category, or time period. Think about both leading and lagging indicators, guardrail metrics, and unintended consequences. Discuss how you'd validate findings, what confidence level you'd need before recommending action, and how you'd present findings to inspire stakeholder action. Mention data quality considerations and assumptions clearly. Be prepared to pivot your analysis based on interviewer feedback—adaptability and intellectual humility are valued at senior levels.
Focus Topics
Diagnostic Analysis and Root Cause Investigation
Systematic approaches to diagnosing unexpected business changes. Breaking down metrics by dimensions, conducting segmentation analysis, forming and testing hypotheses about underlying causes. Knowing what questions to ask to narrow search space.
Practice Interview
Study Questions
Communicating Insights and Driving Action
Translating complex analytical findings into clear, compelling narratives for non-technical business partners. Knowing when to include technical detail vs. simplify. Using visualization and storytelling to support conclusions. Tailoring communication to audience (executives vs. operations teams).
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Trade-Offs, Prioritization, and Pragmatic Decision-Making
Discussing trade-offs between competing metrics, analytical precision vs. speed to insight, investments in data quality vs. imperfect data available now. Making recommendations when perfect information isn't available. Knowing when 'good enough' data supports a decision.
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Product Sense and Business Context Understanding
Understanding Amazon's business model, competitive dynamics, customer behavior, and how analytical insights drive product and business decisions. Thinking about customer impact, long-term business value, and organizational incentives.
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Metric Definition and North Star KPIs
Defining primary success metrics for business initiatives. Understanding leading vs. lagging indicators, guardrail metrics, and how metrics align with strategic business objectives. Considering unintended consequences and avoiding metric gaming.
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Study Questions
Onsite Round 4 - Behavioral and Leadership Excellence
What to Expect
A 60-minute round with the hiring manager or a senior team member focused on assessing behavioral fit with Amazon's Leadership Principles and your readiness for senior-level responsibilities.[1][2] You'll be asked 2-3 deep-dive behavioral questions about projects where you demonstrated ownership, overcame challenges, and delivered measurable impact. Example questions: 'Tell me about a time you discovered a major data quality issue. How did you investigate and what long-term solution did you implement?', 'Describe a situation where you had to mentor a junior analyst on a complex topic. How did you approach it?', or 'Tell me about a time you disagreed with a business stakeholder on an analytical approach. How did you handle it?' For senior candidates, expect questions about leadership, influencing without direct authority, navigating organizational ambiguity, handling conflict or resistance to your recommendations, and how you've raised the bar for your team.
Tips & Advice
Use the STAR method (Situation, Task, Action, Result) for all stories, being specific about your role vs. others' roles and emphasizing your decision-making. For senior candidates, emphasize ownership, personal agency, and business impact. Include quantified metrics and business outcomes. Connect each story explicitly to Amazon Leadership Principles like 'Ownership', 'Dive Deep', 'Deliver Results', 'Earn Trust', 'Are Right, A Lot', and 'Invent and Simplify'. Prepare stories demonstrating: (1) handling ambiguity in data or requirements, (2) mentoring or developing others, (3) making a difficult trade-off or difficult decision, (4) pushing back respectfully on something you disagreed with, (5) failure and learning, (6) improving data governance or analytics infrastructure. Be authentic and specific—vague or generic stories won't resonate. For senior roles, show strategic thinking, influence, leadership maturity, and bias toward action, not just technical execution.
Focus Topics
Data Quality Ownership, Governance, and Building Trust
Example of identifying a significant data quality issue that impacted business decisions, taking personal ownership to fix it, implementing long-term solutions, improving data governance practices, and earning stakeholder trust through transparency and diligence.
Practice Interview
Study Questions
Mentorship, Developing Others, and Raising the Bar
Specific examples of mentoring junior analysts or engineers, helping them grow technical skills, pushing them to deliver quality work, and raising overall team capabilities. Describing your approach to developing talent and creating a culture of excellence.
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Study Questions
Amazon Leadership Principle: Deliver Results
Stories about achieving ambitious goals despite constraints, prioritizing what matters most, maintaining high quality standards, and delivering consistently on commitments. Examples of delivering quality work on tight deadlines or resource constraints.
Practice Interview
Study Questions
Amazon Leadership Principle: Ownership
Stories demonstrating accountability for end-to-end outcomes beyond your job description. Examples of taking initiative, following through on commitments despite obstacles, thinking long-term about impact, and not waiting for others to fix problems. Taking responsibility for team or domain-level improvements.
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Study Questions
Amazon Leadership Principle: Dive Deep
Examples of questioning assumptions, investigating root causes thoroughly, and not accepting surface-level explanations. Stories showing meticulous attention to detail, rigor in analytical work, and commitment to understanding before recommending action.
Practice Interview
Study Questions
Onsite Round 5 - Bar Raiser Round
What to Expect
The final 60-minute round conducted by a 'Bar Raiser'—typically a very senior engineer, principal-level contributor, or manager from another team at Amazon.[1] This round comprehensively evaluates your leadership potential, innovation capability, strategic thinking, and alignment with Amazon's highest standards. Bar Raisers assess whether you will raise the bar for Amazon and the team, elevating standards and capabilities, not just meeting current expectations. You'll be asked about ambitious initiatives you've led, times you've influenced decisions at higher organizational levels, how you approach innovation and calculated risk-taking, situations where you challenged the status quo, and your vision for the future of analytics or your domain. This round evaluates not only technical depth but demonstrated strategic influence, vision, leadership maturity, and potential for future growth into even more senior roles.
Tips & Advice
The Bar Raiser round is about demonstrating exceptional leadership and raising the bar, not just competence. Prepare stories showing you've driven significant, measurable impact, influenced organizational decisions or strategy, or brought innovative approaches that changed how your team works. Quantify your impact in business terms (revenue generated or protected, efficiency gains, time saved, improved decision quality, talent developed). For senior BIEs, discuss how your analytics work influenced product decisions or business strategy, not just enabled operational reporting. Mention times you questioned conventional thinking and proposed a better approach that was adopted. Show intellectual curiosity about emerging analytics capabilities or technologies. The Bar Raiser wants evidence that you think strategically and will continue to grow into even more senior roles. Be ready to discuss your vision for analytics in your domain and how you'd approach emerging challenges (e.g., real-time analytics, ML integration, data democratization). Show genuine customer obsession—how does your work ultimately drive better outcomes for Amazon's customers? Demonstrate strategic patience combined with bias for action.
Focus Topics
Amazon Leadership Principle: Think Big
Examples of proposing ambitious goals or multi-year initiatives, envisioning how your analytics domain could evolve, or tackling complex problems others considered unsolvable. Balancing big thinking with pragmatic execution.
Practice Interview
Study Questions
Amazon Leadership Principle: Customer Obsession
Demonstrating how you think about end-customer impact in your analytics work. Examples of decisions made with deep understanding of customer needs and Amazon's customer-centric philosophy. Thinking about how analytics ultimately serves the customer.
Practice Interview
Study Questions
Influencing and Decision-Making at Organizational Level
Stories about influencing stakeholders' decisions or perspectives through data-driven insights, navigating disagreements or resistance, and persuading others to adopt your recommendations or direction without direct authority.
Practice Interview
Study Questions
Strategic Impact Through Analytics Leadership
Examples where your analytics work directly influenced strategic product or business decisions at multiple organizational levels, not just operational reporting. Demonstrating how you identified opportunities using data and communicated insights that shaped direction.
Practice Interview
Study Questions
Innovation and Calculated Risk-Taking
Examples of proposing novel analytical approaches, adopting new tools or methodologies, or challenging established practices that yielded better results. Describing the reasoning, risks considered, potential downsides weighed, and measurable outcomes achieved.
Practice Interview
Study Questions
Frequently Asked Business Intelligence Analyst Interview Questions
Sample Answer
-- user_events(user_id bigint, event_date date)Sample Answer
WITH monthly_users AS (
SELECT
DATE_TRUNC('month', event_date) AS month_start,
user_id
FROM user_events
GROUP BY 1, 2
),
cohorts AS (
SELECT
month_start,
COUNT(DISTINCT user_id) AS cohort_size
FROM monthly_users
GROUP BY month_start
),
retained AS (
SELECT
m.month_start,
COUNT(DISTINCT m.user_id) AS retained_next_month
FROM monthly_users m
JOIN monthly_users n
ON m.user_id = n.user_id
AND n.month_start = m.month_start + INTERVAL '1 month'
GROUP BY m.month_start
)
SELECT
TO_CHAR(c.month_start, 'YYYY-MM') AS month,
c.cohort_size,
COALESCE(r.retained_next_month, 0) AS retained_next_month
FROM cohorts c
LEFT JOIN retained r USING (month_start)
ORDER BY c.month_start;Sample Answer
-- 1. Extract identity edges from new events
INSERT INTO identity_edges (id_a, id_a_type, id_b, id_b_type, weight, ts)
SELECT DISTINCT x.id, x.type, y.id, y.type,
CASE WHEN x.user_id IS NOT NULL AND y.email_verified THEN 100
WHEN x.session_id = y.session_id THEN 80
ELSE 10 END AS weight,
MAX(ev.event_time)
FROM events ev
CROSS APPLY (VALUES (ev.user_id,'user'), (ev.anon_id,'anon'), (ev.email,'email'), (ev.device_id,'device')) x(id,type)
CROSS APPLY (VALUES (ev.user_id,'user'), (ev.anon_id,'anon'), (ev.email,'email'), (ev.device_id,'device')) y(id,type)
WHERE x.id IS NOT NULL AND y.id IS NOT NULL AND NOT (x.id = y.id AND x.type = y.type);
-- 2. Aggregate and threshold (keep strong edges or repeated weak edges)
WITH agg AS (
SELECT id_a,id_a_type,id_b,id_b_type, SUM(weight) as score
FROM identity_edges WHERE ts >= CURRENT_DATE - interval '30 days'
GROUP BY 1,2,3,4
)
INSERT INTO filtered_edges (...)
SELECT ... FROM agg WHERE score >= 50; -- threshold
-- 3. Compute connected components (example using recursive SQL / union-find table)
WITH RECURSIVE components AS (
SELECT id_a AS node, id_a AS root FROM filtered_edges
UNION
SELECT fe.id_b, c.root FROM filtered_edges fe JOIN components c ON fe.id_a = c.node
)
SELECT node, MIN(root) OVER (PARTITION BY node) AS canonical_person_candidate
... -- assign or upsert into person_map table with deterministic person_id = hash(min_root)Sample Answer
Sample Answer
-- PostgreSQL example
SELECT
session_id,
view_ts,
page_url,
session_event_number,
EXTRACT(EPOCH FROM (view_ts - prev_view_ts))::INT AS time_diff_seconds
FROM (
SELECT
session_id,
view_ts,
page_url,
-- deterministic ordering: view_ts then page_url as tie-breaker
ROW_NUMBER() OVER (
PARTITION BY session_id
ORDER BY view_ts, page_url
) AS session_event_number,
LAG(view_ts) OVER (
PARTITION BY session_id
ORDER BY view_ts, page_url
) AS prev_view_ts,
COUNT(*) OVER (PARTITION BY session_id) AS events_in_session
FROM page_views
) t
WHERE events_in_session > 1
ORDER BY session_id, session_event_number;Sample Answer
Sample Answer
Sample Answer
Sample Answer
Sample Answer
SELECT
customer_id,
SUM(amount) AS total_spent
FROM sales
GROUP BY customer_id;SELECT
customer_id,
sale_date,
amount,
SUM(amount) OVER (
PARTITION BY customer_id
ORDER BY sale_date
ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
) AS running_total
FROM sales
ORDER BY customer_id, sale_date;Search Results
Amazon Business Intelligence Engineer Interview Questions
Here is a comprehensive guide to ace Amazon business intelligence engineer interview covering each step from typical interview questions to SQL problems.
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