Its no secret that Amazon is one of the largest companies in the world. Even during the worst of COVID-19, they (briefly) achieved a TRILLION-dollar market capitalization and have grown to over one million employees. Its a complex business featuring dozens of departments, countless teams, and seemingly infinite data. Who analyzes this mountain of information, discovering critical insights driving the biggest decisions? Amazons Business Intelligence Engineers, or BIEs for short.
Amazons BIEs design and build out the many analytics, essential KPIs, and data pipelines while also drafting reports, data dashboards, and visualizations. BIE positions require a significant grasp of statistics, data warehousing, and the Extract, Transform, Load data copying procedure. As is often the case with data analytics positions, proficiency in SQL is required of Amazon Business Intelligence Engineers.
Are you looking to join Amazons ranks as a Business Intelligence Engineer? Lets go ahead and take a closer look at the interview process for BIEs.
Similar to other technical interview loops at Amazon, the BIE interview generally consists of two phone interviews followed by an on-site round.
The first step in the BIE interview process is an initial discovery screening. Most offen this will be with a recruiter, but you may also have an additional screening with a team member or hiring manager – this seems to differ among locations. The focus of this call is primarily to gauge whether you would be a good fit. You can expect basic questions focused on past work experience. Make sure to prepare by reviewing your resume closely, as youll probably be asked to elaborate on some key points. Most importantly, your interviewer will allow you to detail the reasons why you want to join the specific team you applied to. Ultimately, youll need to provide an answer impressive enough that they forward you to the next stage.
Afterward, youll be asked to complete a live technical screening to test your skills in SQL and Python. This wont be the last technical round, but its meant to test your basic technical prerequisites. You should be prepared to answer at least five SQL, Python, data visualization, and/or business analytics questions. In preparing for this round, practice presentation skills and business acumen – its just as important to communicate actionable recommendations to your interviewers as it is to code well. Demonstrate your ability to leverage corporate data to make insightful business decisions, and youll have a good shot of advancing.
The on-site consists of 5-6 individual rounds with business intelligence engineers, data scientists, and a hiring manager. Each will last 45 minutes to 1 hour. Expect more in-depth questions and be prepared to provide details. In many ways, the on-site is practically an expanded and more substantive screening. Given the length and number of the interview rounds, you may be wondering what the daily schedule would look like. Usually, the interviews begin around 10 AM and last until 4 PM, with an hour lunch break in between.
You will be asked numerous questions spanning both data science and business analytics during your meetings with BIEs and data scientists. Youll be asked questions about qualifying requirements, checking edge cases, and expected to complete whiteboarding problems with the BIEs and data scientists. Many of the questions will be based on real business problems that Amazon has faced in the past, so study Amazons structure and history beforehand. Aspiring BIEs also must be prepared for the inevitable series of behavioral questions characteristic of an Amazon interview. These questions, especially, will be highly focused on Amazons Leadership Principles. Reviewing these are an absolute must for any aspiring Amazon employee. Like the initial technical screen, youll be asked a series of statistics, SQL, and other data science questions by the Business Intelligence Engineers. Your interviews with the data scientists will also consist of statistics and data science questions. They will also include questions about more general data science concepts.
Youll meet with several Amazon employees during your on-site interview. Each interviewer has an equal say in the final hiring decision, except for the designated “bar raiser.” These have a unique veto power, capable of rejecting an otherwise solid interview. The other interviewers will be focused on your particular fit to the role in question, but not the bar raiser. – he or she is focused on your fit at the company. Jeff Bezos is notorious for his love of the word “relentless” and its expected that each hire should be shown up by the next. The “bar raisers” in these interviews are literally gauging whether you meet this standard. To avoid the dreaded veto, candidates will need to demonstrate to their bar raiser that their standards are higher than at least 50% of employees. The interview committee wont make you wait for long after you complete your interviews. You will hear back from them in 24 hours.
Leaders start with the customer and work backwards. They work vigorously to earn and keep customer trust. Although leaders pay attention to competitors, they obsess over customers.
Leaders are owners. They think long term and don’t sacrifice long-term value for short-term results. They act on behalf of the entire company, beyond just their own team. They never say “that’s not my job.”
Leaders expect and require innovation and invention from their teams and always find ways to simplify. They are externally aware, look for new ideas from everywhere, and are not limited by “not invented here.” As we do new things, we accept that we may be misunderstood for long periods of time.
Leaders are right a lot. They have strong judgment and good instincts. They seek diverse perspectives and work to disconfirm their beliefs.
Leaders are never done learning and always seek to improve themselves. They are curious about new possibilities and act to explore them.
Leaders raise the performance bar with every hire and promotion. They recognize exceptional talent and willingly move them throughout the organization. Leaders develop leaders and take seriously their role in coaching others. We work on behalf of our people to invent mechanisms for development like Career Choice.
Leaders have relentlessly high standards — many people may think these standards are unreasonably high. Leaders are continually raising the bar and drive their teams to deliver high-quality products, services, and processes. Leaders ensure that defects do not get sent down the line and that problems are fixed so they stay fixed.
Thinking small is a self-fulfilling prophecy. Leaders create and communicate a bold direction that inspires results. They think differently and look around corners for ways to serve customers.
Speed matters in business. Many decisions and actions are reversible and do not need extensive study. We value calculated risk-taking.
Accomplish more with less. Constraints breed resourcefulness, self-sufficiency, and invention. There are no extra points for growing headcount, budget size, or fixed expense.
Leaders listen attentively, speak candidly, and treat others respectfully. They are vocally self-critical, even when doing so is awkward or embarrassing. Leaders do not believe their or their team’s body odor smells of perfume. They benchmark themselves and their teams against the best.
Leaders operate at all levels, stay connected to the details, audit frequently, and are skeptical when metrics and anecdotes differ. No task is beneath them.
Leaders are obligated to respectfully challenge decisions when they disagree, even when doing so is uncomfortable or exhausting. Leaders have conviction and are tenacious. They do not compromise for the sake of social cohesion. Once a decision is determined, they commit wholly.
Leaders focus on the key inputs for their business and deliver them with the right quality and in a timely fashion. Despite setbacks, they rise to the occasion and never settle.
When Exponent spoke with a few folks who interviewed for the Amazon non-tech PM role, some had reported that they also encountered the following questions
Once your on-site is over, the committee will convene. The main thing they will be discussing is how well you reflected Amazons leadership principles. Each of the interviewers you meet with will be delegated 2-3 leadership principles to evaluate in-depth. Depending on the answers you give, these interviewers may raise red flags to their colleagues. A job offer is increasingly unlikely if too many conflicts between your answers and the leadership principles is found. For example, lets suppose you gave an example of working on a strategic project. It would send a negative signal for the “customer obsession” principle if you devised your approach based on the competition rather than the customer.
If you ace all your interview questions without raising any leadership principle red flags and you escape the bar-raiser veto, you have a good chance of receiving an offer. If so, great work! This hiring decision is made within 24 hours, after which youll receive a phone call from a hiring manager. Here youll have a final opportunity to discuss your job expectations and your salary. There are other possible scenarios if the interviewers didnt think it was a good fit beyond a simple rejection. If your interview was strong enough to impress, other hiring managers from different Amazon teams may want to meet with you. They may find a good fit for you on their teams instead.
Amazons Business Intelligence teams are very technical given the emphasis on data analytics. Aspiring BIEs should prepare for the numerous technical questions theyll be asked during their interviews. You should be capable of answering questions on the optimization of queries based on many different data modeling concepts. Your interviewers will have you writing SQL frequently, along with many questions about edge cases. Be sure to study up the ways to simplify complex queries.
Practice designing analytics and data visualizations for the relevant stakeholders. This is especially true for building data dashboards using tools like QuickSight or Tableau, drafting statistical reports, and establishing the KPIs that best measure Amazons success. Amazon has stated that the company does not have any hard preferences in terms of specific visualization tools you use. They are more interested in the thought process and problem-solving approaches the BIE candidates bring to the table.
During your interviews, youll need to be sure to adequately demonstrate these problem-solving skills through your data analysis, chosen statistical methods, or even A/B testing. Youll need to show off your ability to translate the ambiguous problems facing Amazon into concrete requirements while pulling actionable insights from these data sets. Prepare to answer questions about data warehousing and the ETL procedure.
Be sure to ask plenty of questions during the interview process, especially in the initial phone screenings with the recruiter and hiring manager. Ask them what skills are the most relevant and necessary for the particular BIE team youre applying to. For instance, they may say that the team has emphasized statistics skills. Others may require past experience and proficiency with data warehousing and copying procedures. Keep in mind that something like experience with scripting languages, for example, is desired by some teams while irrelevant to others. Regardless, every aspiring Business Intelligence Engineer will need experience with SQL, data visualization, and business analytics.
Throughout the interview process, youll have the opportunity to use a whiteboard. Whether it be with a real whiteboard or a code-sharing link, whiteboarding is an excellent way for your interviewer to learn about your thought-process.
When solving your technical questions, be sure to write a list of requirements on the board, always asking your interviewer more questions. Before going further, these requirements should be written out.
Keep digging for additional clarification from your interviewer. None of them will be actively trying to trick or deceive you, but these questions may initially be vague or abstract. The vagueness requires candidates to ask more questions and think creatively.
Important Topics to Prepare for the Amazon Business Intelligence Engineer Interview
See below some of the key topics you should prepare for before your Amazon BI engineer interview:Â
Here’s an example, recently featured in The Guardian, of what it sounds like when applying the STAR Method in the interview:
Frequently, Amazon engineers are embedded within teams and work cross-functionally with other teams, aiming to improve overall customer experience. The roles of this position range from implementing solutions through data modeling to providing guidance to business leaders.
At Amazon, the Business Intelligence Engineers work in tandem with clients, analysts, and database developers to translate collected data into business decisions. The solutions created by these engineers assist in the analysis, automation, and reporting of both internal and external client data.
The Amazon business intelligence interview is similar to most other technical interviews at Amazon, consisting of two initial phone interviews followed by an onsite interview.
Amazon is large enough to boast of over 40 departments with more than 100 internal teams within these departments. Therefore, it is crucial for Amazon to efficiently process and analyze the huge amounts of corporate data it receives. Business Intelligence Engineers design software and corporate platforms to do exactly this, thereby making it easier to draw meaningful conclusions from Amazon’s collected data. They work within teams and alongside Amazon’s internal clients to provide accurate and accessible data that supports critical business processes.
What to Expect in the Amazon Business Intelligence Engineer Interview?
In your Amazon Business Intelligence Engineer interview, you can expect:
Each round usually focuses on one or two skills, although you can expect questions related to other skills to bleed in from time to time. With that in mind, you can typically expect: