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Amazon is one of the largest online markets in the world, and unlike the traditional market place, Amazon is gigantic! and it is filled with millions and millions of products on display. In the USA only, Amazon controls more than half of the online market, and since its inception in 1994, it has worked to achieve its ultimate goal which is being “the one-stop-shop” by learning from data.
In this age of data, Amazon collects data on every customer clicks and interactions on its website; this includes what items customers are looking at, what items they put in their cart, what quality they want, what their preferences are, etc. Amazon compiles this data and feeds it to its recommendation system to better serve customers needs and improve the shopping experience by recommending products that best fit their preferences. Amazon also leverages data to make business decisions and drive growth. Data analyst at Amazon work with both technical and non-technical internal teams to build the right analysis to answer key business questions
The Data Analyst Role at Amazon
Data analysts at Amazon help bridge the gap between data and the decision-making process. Typical data analyst roles at Amazon include data analysis, dashboard/report building, and metric definitions and reviews. Data analysts at Amazon also design systems for data collection, compiling, analysis, and reporting.
Data analyst roles differ based on the type of data they are working with (e.g Twitch data, Sales data, Alexia data, etc.), the type of project they are on, the product they're working with, and the team their assigned to. Data analyst at Amazon also collaborate cross-functionally with various teams including engineering, data science, and marketing to provide data-driven insights to research and business areas. Depending on the team, the role may range from basic business intelligence analytics such as data processing, analysis, and reporting to a more technical role like data collection.
The data analyst position at Amazon requires specialization in knowledge and experience. Therefore, Amazon only hires highly qualified candidates with at least 3 years of industry experience working with data analysis, data modelling, advanced business analytics, and other related fields.
Other basic qualifications include:
- Bachelor's or Masters (PhD prefered) in Finance, Business, Economics, Engineering, math, statistics, computer science, Operation Research, or related fields.
- Experience with scripting, querying, and data warehouse tools, such as Linux, R, SAS, and/or SQL
- Extensive experience in programming languages like Python, R, or Java.
- Experience with querying relational databases (SQL) and hands-on experience with processing, optimization, and analysis of large data set.
- Proficiency with Microsoft Excel, Macros and Access.
- Experience in identifying metrics and KPIs, gathering data, experimentation, and presenting decks, dashboards, and scorecards.
- Experience with business intelligence and automated self-service reporting tools such as Tableau, Quicksight, Microsoft Power BI, or Cognos.
- Experience with AWS services such as RDS, SQS, or Lambda.
Data Analyst Teams at Amazon
Amazon is a large conglomerate technology company offering many products and services. As a result of this, Amazon has over 100 teams working on various areas. Data analysts work with these teams to help bridge the gap between data and the decision making process. Generally, data analysts at Amazon help streamline the decision making process through the analysis of data.
Depending on the team at Amazon, data analysts' responsibilities may include:
- Alliance (Twitch): Leveraging advanced analytics in shaping the way deals performance is measured, defining what questions should be asked, and scaling analytics methods and tools to support Twitch’s growing business. Also, define and track KPIs, support strategic initiatives, evaluate new business opportunities, and improve/enhance decision making through data.
- Finance Operation: Develop standard and ad hoc analysis and report for decision making. Structure high-level business problems within the framework of analyzing, defining, creating and sourcing the data, producing metrics, and providing recommendations. Automate standard reporting and drive data governance and standardization.
- Search Capacity: Leverage advance analytics and predictive algorithms to create powerful, customer-focused search solutions and technologies. Collaborate with engineering and operation teams to scale Amazon search service by identifying and tracking KPIs regarding efficiency and cost.
- Textbook team: Build robust data analytics solutions to improve the customer experience. Employ advanced data mining concepts, data modelling, and analytics to define and measure metrics for evaluating business growth. Extract, integrate and work on critical data to build data pipelines, automate reports and dashboards, and leverage self-service tools to internal stakeholders.
- Fraud and Abuse prevention: Leverage sophisticated machine learning concepts to mitigate and prevent fraud. Develop and manage scalable solutions for new and existing metrics, reports, analysis, and dashboards to support business needs. Implement customized ETL pipelines from diverse sources for higher data quality and availability
- Buying: Use advanced analytics concepts to determine how much inventory to carry on all of Amazon’s websites worldwide. Develop and maintain metrics, expose and measure the current performance of Amazon's buying system, identify and quantify opportunities for improvement, and leverage Amazon's massive data to identify and prevent unexpected performance. Collaborate cross-functionally with other teams, especially engineering, research, data science and business teams for future innovation.
- Engineering Success (Twitch): Collaborate with the engineering team at Twitch to provide data analysis towards improving and shaping success measurement metrics, defining business-impact questions, and scaling analytics methods and tools to bolster Amazon's growing business.
The Interview Process
The Amazon data analyst interview process follows the standard Amazon “STAR” (Situation, Task, Action, and Result) process with slight variations. The interview process starts with an initial phone screen with HR. After this, a technical interview will be scheduled. Once you get through the technical interview, a final onsite interview with 5 to 6 one-on-ones with the hiring manager, team members, and HR will be scheduled.
This is a standard introductory interview with HR after the submission of an application. The interview is exploratory and lasts about 45 minutes; it focuses on showcasing your background, skillsets, and work experience related to the position. You also get to know about Amazon's work culture and the position.
Note: Amazon emphasizes its leadership principles. It will be really helpful to tailor your responses to follow the “STAR” format based on Amazon's leadership principles.
- What is the biggest challenge you have overcome?
- What is your previous experience with SQL?
- Tell me about a time when you disagreed with a manager. How did you handle the situation? What was the result?
- How would you go about making improvements (performance, safety, process) in your workplace?
- Describe a long term goal and how you plan to achieve it.
This is a technical interview with a member of HR or a manager. Amazon uses a collaboration service platform called “CollabEdit” for all its technical interviews.
The questions in this interview round revolves around a SQL coding challenge, Excel, and questions regarding Amazon's Leadership Principles (LP). It's definitely helpful to practice the Amazon SQL questions on Interview Query.
The onsite interview for a data analyst at Amazon is very similar to other onsite interviews at the company. Candidates who progress to this stage of the interview process go through 5 or 6 one-on-one interviews with a hiring manager, a team manager, data analysts, data engineers, and statisticians. There is a lunch break in between interview rounds. The Amazon data analyst onsite interview rounds are comprised of data science concepts, SQL coding, and the famous Amazon Leadership Principles.
Data Analyst Interview Notes and Tips
The Amazon data analyst interview primarily consists of data science concepts. It is uniquely structured to asses a candidate's ability to analyze Amazon’s data to provide new insights that will shape business decisions. Leveraging Amazon's “STAR” format in answering questions can give you an advantage. To better understand Amazon's STAR process, check out Amazon's BI engineer interview process on Interview Query.
Interviewers at Amazon are looking for you to support your answers with your previous work experience. Attempt to answer each question with examples from past work experience; this may include the challenges you faced, what method or approach you used, and how you overcame those challenges.
Amazon Data Analyst Interview Questions:
- Which functions in SQL do you like the most?
- Explain OLAP cubes and a use case explaining business analytics application.
- What are data normalization and non-normalization?
- What happens to the data of a table with foreign keys when the associated table with primary keys has been updated?
- What do you understand by cascading referential integrity?
- Explain the difference between the linear and logistic regression and use examples.
- What is an independent variable and what if I have three independent variables in my model and no dependent variable?
- Write an equation for the multivariance or multiple regression model.
- Given a sample with n observations, how could you test a hypothesis?
- What are the Assumptions of ANOVA.
- What test would you use for a small sample?
- What is the null hypothesis?
- What are type 1 and type 2 errors?
- Use the following tables to write a query to retrieve data for customers who registered in the past ten days and spent over $100. Write another query to retrieve data for customers that spent over $100 in the past seven days. The first table is a customer purchase table with five columns: customer id, purchase date, product id, unit price, and units p urchased. The second table is a customer details table with two columns: customer id and registration date.
- What is the probability of generating ten consecutive numbers in ascending order out of 100 numbers?
- How would you merge two tables in SQL?
- Write a function to calculate the Fibonacci code in any of these languages (VBA, Python, Java).