Fractal Analytics Data Engineer Interview Questions + Guide in 2024

Fractal Analytics is a leading AI partner for Fortune 500 companies with a mission to empower every human decision in the enterprise. Recognized as a Great Place to Work and a Gartner Cool Vendor, Fractal champions individual freedom, diversity, and creativity.

As a Data Engineer at Fractal, you will tackle complex analytics problems, develop and maintain large-scale data solutions, and contribute to transformative projects. This position requires strong skills in SQL, Python, AWS, ETL processes, and Spark and offers the chance to make impactful contributions to advanced analytics initiatives.

This guide will walk you through the interview process, equip you with tips on answering commonly asked Fractal Analytics data engineer interview questions, and provide tips to help you succeed. Let’s get started!

The interview process usually depends on the role and seniority; however, you can expect the following on a Fractal Analytics data engineer interview:

Application Review and Initial Contact

Once your application is submitted, a recruiter from Fractal Analytics will review it. If your CV happens to be among the shortlisted few, you will be contacted to verify key details about your experiences and skill level. The initial contact will include questions about your availability and interest in the role. This stage generally takes about 15-20 minutes.

Online Coding Test

The next step typically involves an online coding test to assess your foundational knowledge and problem-solving skills. The test will cover Python and SQL and may include questions related to data structures, algorithms, and aptitude. You will have a limited time frame to complete this test, often around 1-2 hours.

Technical Interviews

Round 1: Technical Screening

The first technical interview generally revolves around your previous experiences and projects. You will be asked to explain your current project’s data pipeline, and questions will delve into Big Data basics and coding in Python, PySpark, and SQL. This round is also likely to include scenario-based questions, such as:

• Drop duplicate rows using SQL and PySpark
• How many stages are created in Spark for reading a file, transforming, and writing to a file?
• Explain window functions in SQL

This interview usually lasts for about 1 hour.

Round 2: Deep-Dive Technical

The second technical round aims to deepen your technical expertise. Topics could include Spark architecture, optimization, AWS, Azure Databricks, and Data Factory. Other possible questions may involve coding challenges or real-world scenarios you may face as a Data Engineer. This phase also typically spans one hour.

Manager Round

A managerial interview will be held if you advance past the technical rounds. This round assesses your behavioral acumen, team fit, and problem-solving mindset. You might be asked about scenarios involving support processes, behavioral issues, and your approach to resolving them. Questions about your career journey and ability to handle a managerial role may also be included. This session typically lasts 30-45 minutes.

HR Discussion

The final step is a meeting with the HR team to discuss your package, benefits, and other administrative details. This conversation will address intricate questions regarding company policies and your preferred job compensation. This stage can also be an opportunity to negotiate your offer based on any counter-offers you may have. The HR discussion usually lasts 15-30 minutes.

Typically, interviews at Fractal Analytics vary by role and team, but commonly, Data Engineer interviews follow a fairly standardized process across these question topics.

1. Select the top 3 departments with at least ten employees and rank them by the percentage of employees making over 100K.

Given the tables for employees and departments, select the top 3 departments with at least ten employees and rank them according to the percentage of their employees who make over 100K in salary.

2. When would you use a bagging algorithm versus a boosting algorithm?

Compare two machine learning algorithms and determine when to use a bagging or boosting algorithm. Provide examples of the tradeoffs between the two.

3. How would you design a model to map legal first names to likely nicknames?

As a data scientist at Facebook, design a machine learning model that can map the legal first name of a person to likely nicknames they might have.

4. How would you create a system to detect firearm listings on a marketplace?

Design a system to automatically detect if a listing on your website’s marketplace sells a gun in compliance with the Terms of Service Agreement and legal regulations.

5. How would you tackle multicollinearity in multiple linear regression?

Explain the methods you would use to address multicollinearity in a multiple linear regression model.

6. How would you set up an A/B test for button color and position changes?

A team wants to A/B test multiple changes in a sign-up funnel, such as changing a button from red to blue and/or moving it from the top to the bottom of the page. How would you set up this test?

7. How would you forecast Facebook’s revenue for the next year?

You work on the revenue forecasting team at a company like Facebook. An executive asked how much revenue Facebook would make in the coming year. How would you forecast this?

8. How would you investigate if the redesigned email campaign increased conversion rates?

An E-commerce store’s new marketing manager redesigned the new-user email journey, and conversion rates increased from 40% to 43%. However, the rate was 45% a few months prior. How would you determine if the redesign caused the increase?

9. How would you ensure data quality across different ETL platforms for PayPal’s market research?

PayPal partnered with a local survey platform for market research in Southern Africa. The data includes pre-quantified and text data in different languages. How would you ensure data quality across ETL pipelines connecting PayPal’s data marts with the survey platform’s data warehouses?

10. How would you conduct an experiment to test Uber’s new ETA range feature?

A PM at Uber is considering a new feature that displays an ETA range (e.g., 3-7 minutes) instead of a direct estimate (e.g., 5 minutes). How would you conduct this experiment and determine if the results are significant?

11. What is the mean and variance of the distribution of 2X - Y given X and Y are independent normal variables?

Given X sim N(3, 2^2) and(Y sim N(1, 2^2), calculate the mean and variance of the distribution of 2X - Y.

A few tips for acing your Fractal Analytics interview include:

1. Be Technically Prepared: Fractal Analytics significantly focuses on Big Data technologies such as Spark, Hive, AWS, and Databricks. Make sure you brush up on these topics and be ready to tackle questions related to them.

2. Clear Communication: During technical and managerial rounds, clarity and precision in answering questions play crucial roles. Explain your thought process and how you approach solving complex problems.

3. Behavioral Insight: Fractal Analytics values team collaboration and problem-solving capabilities. Be prepared to answer questions about your past experiences dealing with work-related challenges and how you contributed to team success.

What is the average salary for a Data Engineer at Fractal Analytics?

According to Glassdoor, data engineers at Fractal Analytics earn between $97K to $141K per year, with an average of $117K per year.

What technical skills are required for a Data Engineer role at Fractal Analytics?

Candidates should have strong knowledge of SQL, Python, and big data technologies like Spark and Hadoop. Experience with cloud platforms like AWS or GCP, especially services like Databricks, Azure Data Lake, and Azure Data Factory, is highly desirable. Knowledge of data warehousing concepts, ETL processes, and the ability to write optimized queries is crucial.

What kind of projects or tasks will I be working on as a Data Engineer at Fractal Analytics?

As a Data Engineer, you will build data pipelines, design and develop data models, integrate data from multiple sources, perform ETL processes, and collaborate with teams to implement scalable data solutions. You will also work on optimizing and maintaining big data systems to ensure data integrity and performance.

Can you describe the company culture at Fractal Analytics?

Fractal Analytics emphasizes a culture of creativity, collaboration, and continuous learning. They value diversity and independence. The company is also marked by its innovative approach to solving business problems and creating business insights through the power of data. Employees often express appreciation for the supportive and engaging work environment.

Fractal offers an exciting opportunity if you aim to be an integral part of a team that leverages AI to power Fortune 500 companies and thrives on creativity and innovation. Highlight your technical prowess, showcase your project experiences, and be ready to engage in a smooth, professional interview journey.

Ready to be a Fractalite? Dive into the world of transformative data solutions and apply now! Our main Fractal Analytics interview guide can also help you ace any position!

Good luck with your interview!