Babel Street illuminates identity and information for a safer, more productive world with its AI-powered products that transform data into actionable insights. Serving industries like financial services, healthcare, and law enforcement, Babel Street's advanced data and text analytics platform helps customers act with confidence.

As a Data Engineer, you will collaborate with discrete engineering teams to provide annotated, reliable data for training, developing, and evaluating natural language processing systems. Responsibilities include managing large-scale text mining projects, training contractors, and ensuring high-quality data conversion. Strong scripting abilities, knowledge in NLP applications, and experience with manual annotation tools are essential.

Join Babel Street and contribute to cutting-edge software for Natural Language Processing and Text Analytics. Explore this guide for help navigating the interview and landing your role at Babel Street!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Babel Street as a Data Engineer. Whether you were contacted by a Babel Street recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Babel Street Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the Babel Street hiring manager may stay present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the Babel Street Data Engineer role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around Babel Street’s data systems, ETL pipelines, and SQL queries.

In the case of data engineering roles, take-home assignments regarding data cleaning, data annotation, and script automation may be incorporated. Apart from these, your proficiency in Python, XML/JSON parsing, and web scraping may also be assessed during this round.

Depending on the seniority of the position, case studies and similar real-scenario problems may also be assigned.

Onsite Interview Rounds

Following a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds, varying with the role, will be conducted during your day at the Babel Street office. Your technical prowess, including programming, data mining, and NLP capabilities, will be evaluated throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the Data Engineer role at Babel Street.

Quick Tips For Babel Street Data Engineer Interviews

• Know Your Babel Street Products: Babel Street interviews often focus on their core products. Study Babel Street’s data analytics and intelligence platform and understand how you could contribute to it.
• Be Data-Driven: Babel Street’s data engineer interviews assess your capability to handle large-scale data projects. Brush up on your knowledge of data parsing, NLP applications, and annotation tools.
• Embrace the Mission: Babel Street values mission-critical applications and actionable insights. Practice responding to behavioral questions with answers that reflect Babel Street’s mission of transforming data into knowledge.

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

• Write a SQL query to select the 2nd highest salary in the engineering department. Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

• Write a function to merge two sorted lists into one sorted list. Given two sorted lists, write a function to merge them into one sorted list. Bonus: What's the time complexity?

• Write a function missing_number to find the missing number in an array. You have an array of integers, nums of length n spanning 0 to n with one missing. Write a function missing_number that returns the missing number in the array. Complexity of (O(n)) required.

• Write a function precision_recall to calculate precision and recall metrics from a 2-D matrix. Given a 2-D matrix P of predicted values and actual values, write a function precision_recall to calculate precision and recall metrics. Return the ordered pair (precision, recall).

• Write a function to search for a target value in a rotated sorted array. Suppose an array sorted in ascending order is rotated at some pivot unknown to you beforehand. You are given a target value to search. If the value is in the array, then return its index; otherwise, return -1. Bonus: Your algorithm's runtime complexity should be in the order of (O(\log n)).

• Would you suspect anything unusual about the A/B test results with 20 variants? Your manager ran an A/B test with 20 different variants and found one significant result. Would you consider this result suspicious?

• How would you set up an A/B test to optimize button color and position for higher click-through rates? A team wants to A/B test 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 design this test?

• What steps would you take if friend requests on Facebook are down 10%? A product manager at Facebook reports a 10% decrease in friend requests. What actions would you take to investigate and address this issue?

• Why might job applications be decreasing while job postings remain constant? You observe that the number of job postings per day has remained stable, but the number of applicants has been decreasing. What could be causing this trend?

• What are the drawbacks of the given student test score datasets, and how would you reformat them for better analysis? You have data on student test scores in two different layouts. What are the drawbacks of these formats, and what changes would you make to improve their usefulness for analysis? Additionally, describe common issues in "messy" datasets.

• Is this a fair coin given it comes up tails 8 times out of 10 flips? You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if the coin is fair based on this outcome.

• How would you write a function to calculate sample variance for a list of integers? Write a function that outputs the sample variance given a list of integers. Round the result to 2 decimal places. Example input: test_list = [6, 7, 3, 9, 10, 15]. Example output: get_variance(test_list) -> 13.89.

• Is there anything suspicious about an A/B test with 20 variants where one is significant? Your manager runs an A/B test with 20 different variants and finds one significant result. Would you find anything suspicious about these results?

• How would you find the median of a list where more than 50% of the elements are the same? Given a list of sorted integers where more than 50% of the list is comprised of the same repeating integer, write a function to return the median value in (O(1)) computational time and space. Example input: li = [1,2,2]. Example output: median(li) -> 2.

• What are the drawbacks of the given student test score data layouts, and how would you reformat them? Assume you have data on student test scores in the layouts shown in Dataset 1 and Dataset 2. Identify the drawbacks of these layouts, suggest formatting changes to make the data more useful for analysis, and describe common problems seen in "messy" datasets.

• How would you evaluate whether using a decision tree algorithm is the correct model for predicting loan repayment? You are tasked with building a decision tree model to predict if a borrower will pay back a personal loan. How would you evaluate if a decision tree is the right choice, and how would you assess its performance before and after deployment?

• How does random forest generate the forest and why use it over logistic regression? Explain the process by which a random forest generates its ensemble of trees. Additionally, discuss why one might choose random forest over logistic regression for certain problems.

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms. Describe scenarios where you would prefer a bagging algorithm over a boosting algorithm, and discuss the tradeoffs between the two.

• How would you justify using a neural network to solve a business problem and explain its predictions to non-technical stakeholders? Your manager asks you to build a neural network model for a business problem. How would you justify the complexity of this model and explain its predictions to non-technical stakeholders?

• What metrics would you use to track the accuracy and validity of a spam classifier for emails? You are tasked with building a spam classifier for emails and have completed a V1 of the model. What metrics would you use to evaluate the model's accuracy and validity?

Joining Babel Street as a Data Engineer is a remarkable opportunity to delve into mission-critical projects that transform massive multilingual data into actionable insights. With a role that spans advanced data acquisition, annotation, and NLP, you'll be at the forefront of innovative text analytics. If you want more insights about the company, check out our main Babel Street Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles that you can explore.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every Babel Street Data Engineer interview question and challenge. You can check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!