Amazon Business Intelligence Engineer Interview Questions

Amazon Business Intelligence Engineer interview questions span both deep technical analytics and Amazon’s behavioral expectations, so the loop tests SQL and ETL instincts alongside product sense and Leadership Principles. What’s distinctive about this role is the blend: you’ll be evaluated for the ability to extract and transform data efficiently, define and defend KPIs, build reliable reports or dashboards, and translate findings into business recommendations. Interview preparation should include practicing advanced SQL (joins, window functions, CTEs, performance thinking), familiarizing yourself with common BI workflows, and preparing crisp STAR stories that show measurable impact. Expect an initial online assessment or take-home SQL challenge, one or two technical phone screens, and then an interview loop of focused conversations that mix SQL/problem-solving, case-style metric design or dashboarding, and behavioral probes tied to Amazon’s Leadership Principles. To prepare, rehearse end-to-end analytics scenarios where you define metrics, justify tradeoffs, sketch data models, and quantify outcomes; time-box SQL practice; and run mock behavioral interviews that emphasize decisions, data, and results.

18 Questions 1 Company08.14.2026
Showing 18 results
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Amazon
Easy
Business Intelligence Engineer

Visualize Trip Duration, Fees, and Survey Outcomes

A mobility service tracks trip duration, trip fee, and a post-trip survey outcome recorded as good or bad. Design a visual analysis that progresses fr...

Analytics & Experimentation
0
0
3 people solved
Aug 14, 2026
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Amazon
Easy
Business Intelligence Engineer

Defend an Experiment Decision and Its Incremental Impact

Describe an A/B test you owned or analyzed. Explain the original decision, your role, the primary metric, and the estimated incremental lift. Then dis...

Behavioral & Leadership
0
0
2 people solved
Aug 14, 2026
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Amazon
Easy
Business Intelligence Engineer

Reason About Composite Join Keys and Predicate Placement

A SQL review prompt says that two relations must be matched on a composite key with columns (key_a, key_b). The query under review joins on only key_a...

Data Manipulation (SQL/Python)
0
0
1 people solved
Aug 14, 2026
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Amazon
Medium
Business Intelligence Engineer

Identify Most Popular First-Watched Movie in Viewing History

MOVIE_VIEWS +------------+--------------------+------------+ | customer_id| title | date | +------------+--------------------+-----...

Data Manipulation (SQL/Python)
7
0
39 people solved
Jul 12, 2025
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Amazon
Medium
Business Intelligence Engineer

Illustrate SQL Join Results with Duplicate Keys

TABLE1 +------+ | col1 | +------+ | 1 | | 1 | | 1 | +------+ ​ TABLE2 +------+ | col1 | +------+ | 1 | | 1 | | 1 | | 1 | | 1 |...

Data Manipulation (SQL/Python)
9
0
26 people solved
Jul 12, 2025
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Amazon
Medium
Business Intelligence Engineer

Analyze Seller Compliance and Customer Purchase Patterns

SELLER_STATUS +-----------+------------+-----------+ | seller_id | date | status | +-----------+------------+-----------+ | 1 |2019-0...

Data Manipulation (SQL/Python)
6
0
25 people solved
Jul 12, 2025
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Amazon
Medium
Business Intelligence Engineer

Calculate Rolling 7-Day Sum of Answers by Device

ANSWERS +------------+--------+---------+ | date | device | answers | +------------+--------+---------+ | 2019-01-01 | echo | 10 | | 2019...

Data Manipulation (SQL/Python)
11
0
47 people solved
Jul 12, 2025
Amazon logo
Amazon
Medium
Business Intelligence Engineer

Identify Unique Unordered City Pairs in Flight Log

FLIGHTS +----------------+---------------+ | departure_city | arrival_city | +----------------+---------------+ | NYC | LAX | | ...

Data Manipulation (SQL/Python)
3
0
8 people solved
Jul 12, 2025
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Amazon
Medium
Business Intelligence Engineer

Find Top Three Books by City in Recent Months

BOOK_SALES +------+--------+------------+------------+ | city | asin | sale_date | units_sold | +------+--------+------------+------------+ | NYC ...

Data Manipulation (SQL/Python)
4
0
24 people solved
Jul 12, 2025
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Amazon
Medium
Business Intelligence Engineer

Compute First Order Proportions by Day and Category

ORDERS +----------+------------+----------+-------------+ | order_id | date | category | customer_id | +----------+------------+----------+-----...

Data Manipulation (SQL/Python)
6
0
23 people solved
Jul 12, 2025
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Amazon
Medium
Business Intelligence Engineer

Identify Frequent Actor-Director Collaborations in Film Database

FILMOGRAPHY +-------+---------+------------+ | actor | dir | date | +-------+---------+------------+ | Tom | Nolan | 2020-01-01 | | Tom ...

Data Manipulation (SQL/Python)
2
0
10 people solved
Jul 12, 2025
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Amazon
Medium
Business Intelligence Engineer

Analyze Top Book Sales and Unique Customer Purchases

BOOK_TRANSACTION +---------------+------------+-------------+------+----------+ | MARKETPLACE_ID| TXN_DAY | CUSTOMER_ID | ASIN | QUANTITY | +------...

Data Manipulation (SQL/Python)
10
0
20 people solved
Jul 12, 2025
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Amazon
Medium
Business Intelligence Engineer

Calculate Weekly Event Sums from Daily Counts

EVENT_LOG +------------+------+ | event_date | cnt | +------------+------+ | 2025-05-01 | 17 | | 2025-05-02 | 12 | | 2025-05-08 | 30 | +-------...

Data Manipulation (SQL/Python)
3
0
11 people solved
Jul 12, 2025
Amazon logo
Amazon
Medium
Business Intelligence Engineer

Identify Top-Buying Customers Daily with SQL Query

PRIME +-------------+------------+----------+ | Customer_ID | Start_Date | End_Date | +-------------+------------+----------+ | 1 | 2025-01-...

Data Manipulation (SQL/Python)
16
0
31 people solved
Jul 12, 2025
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Amazon
Medium
Business Intelligence Engineer

Identify Customers Purchasing Every Product in Catalog

X_PURCHASES +-------------+---------+ | customer_id | pro_key | +-------------+---------+ | 1 | A | | 1 | B | | 2 ...

Data Manipulation (SQL/Python)
5
0
16 people solved
Jul 12, 2025
Amazon logo
Amazon
Medium
Business Intelligence Engineer

Count Customers Buying Both 'Kindle' and 'Alexa'

ITEMS +---------+-----------+ | ITEM_ID | ITEM_NAME | +---------+-----------+ | 101 | Kindle | | 202 | Alexa | | 303 | Fire TV | ...

Data Manipulation (SQL/Python)
12
0
26 people solved
Jul 12, 2025
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Amazon
Medium
Business Intelligence Engineer

Create Monthly Revenue Report for Each Department

DEPARTMENT_REVENUE +------------+---------+-------+ | Department | Revenue | Month | +------------+---------+-------+ | A | 8000 | Jan |...

Data Manipulation (SQL/Python)
13
0
35 people solved
Jul 12, 2025
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Amazon
Medium
Business Intelligence Engineer

Identify Employees with Invalid Department References

EMPLOYEE +-----+-----+--------+ | eid | did | ename | +-----+-----+--------+ | 1 | 10 | Alice | | 2 | 11 | Bob | | 3 | 99 | Carol | +--...

Data Manipulation (SQL/Python)
8
0
15 people solved
Jul 12, 2025

Frequently Asked Questions

How difficult are Amazon Business Intelligence Engineer interview questions?
Amazon Business Intelligence Engineer interview questions are commonly rated as moderately to highly challenging depending on level. Expect rigorous SQL problems that test complex joins, window functions, aggregates, and performance thinking, combined with analytics case scenarios and behavioral questions mapped to Amazon Leadership Principles. Interviewers evaluate technical accuracy, clarity of thought, ability to handle ambiguous requirements, and business impact orientation. Senior roles add system design and data modeling depth and may probe statistical reasoning and experimentation. Focused practice on timed SQL, metric design, and concise STAR stories will make the process much more manageable.
What is the typical interview process and where does Business Intelligence Engineer content appear?
The typical Amazon BIE interview process starts with application and often an online assessment that includes SQL and work style questions. Candidates who pass may have one or two technical phone screens covering SQL, analytics, and leadership principles, then an on-site interview loop of roughly five interviews. Technical rounds commonly contain live SQL exercises, analytics case problems, data modeling questions, and sometimes Python scripting or take-home tasks. Behavioral interviews are threaded throughout and map to Amazon Leadership Principles. Exact emphasis and number of rounds can vary by team and level.
How long should I prepare for an Amazon Business Intelligence Engineer interview and how should I structure that time?
A practical preparation timeline is four to six weeks for comprehensive review, or two to three weeks for accelerated preparation. Start by reinforcing SQL fundamentals, window functions, CTEs, and query performance and then refresh data modeling, ETL patterns, and Python for data manipulation. Midway, practice analytics case problems, metric definition, and basic statistical concepts for experiments. In the final week prioritize timed SQL drills, mock interview loops, and polishing STAR stories with measurable results. Regular feedback from peers or coaches and at least a few full-length mock loops will increase readiness.
What key subtopics should I master for Business Intelligence Engineer interviews at Amazon?
Interviewers commonly probe advanced SQL techniques such as joins, window functions, CTEs, and performance tuning, along with data modeling and warehousing fundamentals. Expect questions on ETL design, pipeline reliability, and automation, as well as metric design, funnel analysis, segmentation, and root-cause diagnosis. For roles involving experimentation, statistical basics like hypothesis testing, confidence intervals, and bias awareness are important. Visualization best practices, Python or scripting for data manipulation, and the ability to communicate insights to stakeholders are also frequently evaluated. Tying technical decisions to business impact is crucial.
What are standout tips and common pitfalls for Amazon Business Intelligence Engineer interviews?
Standout tips include defining metrics and assumptions clearly, narrating your SQL and analysis process out loud, and always connecting findings to business impact. Use STAR to structure behavioral responses and quantify results when possible. Clarify ambiguous prompts, discuss edge cases, and iterate on query optimization while explaining tradeoffs. Common pitfalls are vague metric definitions, ignoring data quality or pipeline reliability issues, and failing to show ownership or cross-functional influence. Be authentic rather than reciting canned answers; interviewers value transparent problem solving and evidence of measurable results.

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