The G2O Business Intelligence interview guide, interview questions, salary data, and interview experiences.

G2O Business Intelligence Interview Guide

<p>Getting ready for an Business Intelligence interview at G2O? The G2O Business Intelligence interview span across 10 to 12 different question topics. In preparing for the interview:</p>
<ul>
<li>Know what skills are necessary for G2O Business Intelligence roles.</li>
<li>Gain insights into the Business Intelligence interview process at G2O.</li>
<li>Practice real G2O Business Intelligence interview questions.</li>
</ul>
<p>Interview Query regularly analyzes interview experience data, and we've used that data to produce this guide, with sample interview questions and an overview of the G2O Business Intelligence interview.</p>


Getting ready for an Business Intelligence interview at G2O? The G2O Business Intelligence interview span across 10 to 12 different question topics. In preparing for the interview:

- Know what skills are necessary for G2O Business Intelligence roles.
- Gain insights into the Business Intelligence interview process at G2O.
- Practice real G2O Business Intelligence interview questions.

Interview Query regularly analyzes interview experience data, and we've used that data to produce this guide, with sample interview questions and an overview of the G2O Business Intelligence interview.
        

Overview

G2O Business Intelligence Salary

<p>We've gathered this data from parsing thousands of interview experiences sourced from members.</p>

We've gathered this data from parsing thousands of interview experiences sourced from members.

G2O Business Intelligence Interview Process

<p>We’re given a table of bank transactions with three columns, <code>user_id</code>, a deposit or withdrawal value (determined if the value is positive or negative), and <code>created_at</code> time for each transaction.</p>

<p>Write a query to get the total three-day rolling average for deposits by day.</p>

<p><em>Note: Please use the format <code>&#39;%Y-%m-%d&#39;</code> for the date in the outout</em></p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>bank_transactions</code> table</p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>user_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>transaction_value</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>dt</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>rolling_three_day</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>


Rolling Bank Transactions

<p>Let’s say you’re working for a social media company trying to grow your user base. You know that people tend to use the same social network as their peers, so you want to target all your growth and marketing efforts on specific age groups so as to take advantage of network effects.</p>

<p>You wish to begin with the age group that is currently generating the most connections (fastest growing).</p>

<p>Write a SQL query to find the average number of accepted friend requests for each age group and order the results in descending order.</p>

<p><em>Note: The average number of accepted friend requests is calculated as the total number of friend requests sent by an age group that was accepted divided by the number of members in said age group.</em></p>

<p><em>We can assume the tables hold valid data in that there are no duplicate friend requests, friend requests are only accepted once, and user A can only accept a friend request from User B after B sends A a friend request.</em></p>

<p><em>The result should be rounded to two decimal places.</em></p>

<h3>Example:</h3>

<p><strong>Input:</strong></p>

<p><strong><code>friend_requests</code></strong></p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td>requester_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>receiver_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>sent_at</td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
<p><strong><code>requests_accepted</code></strong></p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td>accepter_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>requester_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>accepted_at</td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
<p><strong><code>age_groups</code></strong></p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td>user_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>age_group</td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td>age_group</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>average_acceptance</td>
<td>FLOAT</td>
</tr>
</tbody>
</table>


Find the average number of accepted friend requests for each age group that sent the requests.

Avg Friend Requests By Age Group

<p>Suppose your company needs to cut costs due to an economic downturn. During a coffee break, you hear a rumor that a lot of money goes to employees who don’t do their work, and you decide to find out if the rumor is true.</p>

<p>Given two tables: <code>employees</code> and <code>projects</code>, find the sum of the salaries of all the employees who didn’t complete any of their projects.</p>

<p>Clarification:</p>

<p>We will consider a project unfinished if it has no end date (its <code>End_dt</code> is <code>NULL</code>).</p>

<p>Given this, we’ll say an employee didn’t finish any of their projects when:</p>

<ul>
<li>They were assigned at least one project.</li>
<li>None of their projects have an <code>End_dt</code>.</li>
</ul>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>employees</code> table</p>

<table>
<thead>
<tr>
<th>id</th>
<th>salary</th>
</tr>
</thead>

<tbody>
<tr>
<td>INTEGER</td>
<td>FLOAT</td>
</tr>
</tbody>
</table>
<p><code>projects</code> table</p>

<table>
<thead>
<tr>
<th>employee_id</th>
<th>project_id</th>
<th>Start_dt</th>
<th>End_dt</th>
</tr>
</thead>

<tbody>
<tr>
<td>INTEGER</td>
<td>INTEGER</td>
<td>DATETIME</td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>total_slack_salary</th>
</tr>
</thead>

<tbody>
<tr>
<td>INTEGER</td>
</tr>
</tbody>
</table>


Find the total salary of slacking employees.

Slacking Employees Salaries

G2O Business Intelligence Interview Questions

<h3><b>Your future starts here</b></h3>
<p>Imagine being part of a team that helps clients build better relationships with customers. When you join us, you'll help top-notch clients to execute the digital strategies of the future. Every day, we collaborate with clients and each other to provide technology expertise, human-centered design and industry experience to deliver real business results.</p>
<h3>Position Description:</h3>
<p>In this role, you will be responsible for managing the end-to-end data platform development process, including data ingestion, processing, storage, and analysis. This role requires a strong technical background in data platform architecture, cloud computing, and big data technologies. This role carries primary responsibilities as a Platform Lead / Platform Engineer and Azure Platform Admin. Must be able to think big picture, about the entire data landscape as the project executes across various data and analytics platform.</p>
<h3>Role Requirements/Qualifications:</h3>
<ul>
<li>Design, build and configure a highly scalable and robust data platform on Azure and Databricks.</li>
<li>Capable of seamless integration & extensibility of existing cloud services to any new program/project initiative.</li>
<li>Develop and implement data ingestion and processing pipelines using Azure services such as Azure Data Factory, Azure Stream Analytics, and Databricks.</li>
<li>Automate and manage provisioning needs, such as Databricks Cluster Policies, Unity Catalog, Secrets Management, Role Based Access Control model and permissions.</li>
<li>Configure and manage monitoring/alerting around latency performance on the Databricks platform including Delta Live Tables and Databricks Workflows.</li>
<li>Integration experience with modern cloud platforms.</li>
<li>Familiarity with Databricks, Delta Live Tables (DLT), Data Build Tool (DLT), Python, SQL, T-SQL, Spark SQL Queries, Azure Cloud, Azure Data Factory, Delta Lakehouse, SQL Server, Star Schema, Structured/Semi-Structured/Unstructured, OLTP, OLAP, HTAP, ODS, Data Hub.</li>
</ul>
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G2O Business Intelligence Interview Guide

Overview

G2O Business Intelligence Salary

G2O Business Intelligence Interview Process

G2O Business Intelligence Interview Questions

G2O Business Intelligence Jobs