Interview Guide for Data Engineer at Babel Street

About Babel Street

Babel Street illuminates identity and information for a safer, more productive world. Engineered for mission-critical applications, our AI-powered products transform data into knowledge, build a more complete picture around identity, and discover digital evidence. We serve high-stakes industries such as financial services, healthcare, legal, law enforcement, and the global public sector, delivering actionable insights that safeguard lives and protect assets.

About the Data Engineer Role

In this role, you will collaborate with multiple, discrete engineering teams to manage large-scale text mining, data acquisition, and annotation projects. Your expertise will be key in transforming massive amounts of global, multilingual data into actionable and contextual insights.

Skills Required

• Strong scripting abilities (Python)
• Data cleaning, conversion, and organization
• Parsing XML, JSON, tabular data sets
• Scraping and collecting text from online resources
• Knowledge of Linguistics and NLP applications

What Babel Street Looks For

Babel Street seeks candidates who are highly skilled in data engineering and can handle complex data tasks autonomously. Additionally, the ideal candidate should be able to translate product requirements into clear annotation guidelines and possess experience with manual annotation tools.

Welcome to Interview Query

Welcome to the Interview Guide for the Data Engineer position at Babel Street, brought to you by Interview Query. This guide is designed to provide you with the insights and information you need to ace the interview process. Dive deeper to learn how you can enhance your chances of landing this role and join a company that thrives on making impactful data-driven decisions.

Ready to start your journey? Good luck!

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

Submitting Your Application

The first step in joining Babel Street is to submit a compelling application that demonstrates your technical skills and enthusiasm for becoming a part of their innovative team. Make sure to thoroughly review the job description and tailor your CV to complement the key requirements.

Tailoring your CV might involve identifying specific keywords that the hiring manager uses to filter applications and crafting a targeted cover letter. Highlight relevant skills and work experiences in data analytics, natural language processing, and other pertinent areas.

Recruiter/Hiring Manager Call Screening

If your application is shortlisted, a recruiter from Babel Street will get in touch with you to confirm key details such as your experience and skill level. The screening process may include behavioral questions and an initial discussion about your technical background.

Occasionally, the hiring manager might also be present during the screening call. This will give you an opportunity to ask questions about the role and the company, and to undergo a preliminary technical discussion.

This recruiter call typically lasts about 30 minutes.

Technical Virtual Interview

Pass the recruiter screening, and you’ll be invited for a technical virtual interview. This interview will usually consist of video conferencing and screen sharing. The 1-hour session often includes discussions on your ability to handle large-scale text mining, data acquisition, and annotate project oversight.

You can expect questions about your scripting capabilities, especially in Python, as well as your familiarity with parsing XML, JSON, and other tabular data sets. Practical problems related to data cleaning, conversion, and web scraping might also be posed.

If relevant, take-home assignments covering topics like data annotation guidelines, NLP applications, manual annotation tools, and databases like SQL and Mongo, may be assigned.

Onsite Interview Rounds

After a successful virtual interview, you’ll receive another call from the recruiter outlining the next steps, including an invitation to onsite interviews. These rounds usually involve multiple in-depth technical and behavioral interviews conducted at Babel Street’s office.

During these onsite interviews, your technical aptitude in areas such as natural language processing, linguistic annotation, and data management will be gauged. You might also be required to discuss any take-home assignments or present your solutions to real-world problems given prior.

What You Will Do at Babel Street:

• Manage large-scale text mining, data acquisition, and annotation projects.
• Train and supervise contractors performing manual annotation tasks.
• Measure reliability of parallel, manual annotations.
• Survey and catalogue new data releases and best practices in data maintenance, conversion, and analytics.

Skills and Knowledge You Must Bring:

• Strong scripting abilities, particularly in Python.
• Experience with data cleaning, conversion, and organization.
• Proficiency in parsing XML, JSON, and tabular datasets.
• Expertise in scraping and collecting text from online resources, including websites and APIs.
• Capability to write and revise annotation guidelines.
• Knowledge in linguistic and NLP applications such as language identification, tokenization, part-of-speech tagging, morphological analysis, entity extraction, syntactic parsing, and sentiment analysis.
• Experience working with manual annotation tools and platforms like brat, WebAnno, Prodigy, or Mechanical Turk.

Acceptance and Joining

Once you successfully navigate through all interview rounds and are extended an offer, you'll be given details about the company's benefits, which include extensive health benefits, retirement plans, unlimited flexible leave, tuition reimbursement, and more.

Upon accepting the offer, you’ll join Babel Street and begin contributing to their mission to improve global safety and productivity through advanced data analytics and NLP applications. Welcome aboard!

Practice for the Babel Street Data Engineer interview with these recently asked interview questions.