[24]7.ai is at the forefront of leveraging artificial intelligence, human insight, and expertise to enhance customer experience. Their advanced AI platform, backed by over 20 years of contact center operational experience, serves leading global brands by facilitating natural, uninterrupted interactions across various digital and voice channels. Recognized consistently for their excellence, [24]7.ai stands out as a leader in Conversational AI and CXaaS, and in creating inclusive workplaces.

Embarking on a career as a Data Engineer at [24]7.ai means engaging in designing, developing, and maintaining scalable data pipelines. This role involves utilizing tools like Python, Pandas, Spark, and various data platforms. Candidates are expected to collaborate cross-functionally, ensure data integrity, and optimize performance. Strong communication skills and a background in relational databases and cloud platforms are crucial.

In this guide hosted by Interview Query, we'll delve into the Data Engineer interview process at [24]7.ai, covering typical questions, tips, and key insights to help you succeed.

Submitting Your Application

The first step to securing a Data Engineer role at [24]7.ai is to submit a well-tailored application that showcases your technical skills and interest in the company. Carefully review the job description and adjust your CV to match the key qualifications and responsibilities mentioned.

Ensure your CV includes relevant keywords, as hiring managers often use these to filter applications. Additionally, craft a targeted cover letter that highlights your experience with the specific technologies and skills listed by [24]7.ai. Don’t forget to emphasize any work experience that directly aligns with the job role.

Recruiter/Hiring Manager Call Screening

Once your CV is shortlisted, a recruiter from [24]7.ai will reach out to verify your experience and skillset. This initial call will involve basic queries about your background, technical skills, and interest in the role.

Sometimes, the hiring manager may join the call to provide more insights into the role and address any questions you might have. They might also touch upon some technical and behavioral aspects to gauge your suitability for the position.

Typically, this call will last about 30 minutes.

Technical Virtual Interview

Successfully passing the recruiter screening will lead to a technical virtual interview. This stage is likely to be conducted via video conferencing and screen sharing, lasting around 1 hour. During this interview, expect questions focused on data engineering topics such as:

• [24]7.ai’s data systems
• ETL pipelines
• SQL queries
• Data pipeline optimization and troubleshooting

Given the seniority of the role, a practical assessment or case study might also be included. Prepare to discuss real-world scenarios and problem-solving approaches related to data engineering.

Onsite Interview Rounds

If you advance past the technical virtual interview, you will be scheduled for onsite interview rounds. These rounds will be multi-faceted, assessing various competencies and technical abilities.

You may face several interviews throughout the day, each focusing on different aspects such as:

• Programming skills
• Cloud platform experience (e.g., Google Cloud Platform)
• Advanced data engineering techniques
• Collaborative problem-solving

Additionally, if you were given a take-home assignment, you might need to present your solution during one of these rounds.

Quick Tips For [24]7.ai Data Engineer Interviews

Based on interview experiences, here are some tips for aceing your [24]7.ai Data Engineer interview:

• Master the Relevant Technologies: Ensure you have a strong grasp of tools and technologies like Python, Pandas, Spark, and Google Dataflow. Regular practice on Interview Query can help strengthen these skills.

• Understand the Company’s Focus: Familiarize yourself with [24]7.ai’s products and services, particularly their conversational AI platform. This knowledge will help you answer questions more effectively and show your genuine interest in the company.

• Prepare for Behavioral Questions: [24]7.ai values collaboration and problem-solving. Be ready to share experiences where you demonstrated teamwork, leadership, and adaptability in complex situations.

Typically, interviews at [24]7.Ai 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: Determine the time complexity of your solution.

• Create 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. The complexity should be (O(n)).

• Develop a function precision_recall to calculate precision and recall metrics. 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. Write a function to search for a target value in the array and return its index, or -1 if the value is not found. The algorithm's runtime complexity should be (O(\log n)).

• Would you think there was anything fishy about the results of an A/B test with 20 variants? Your manager ran an A/B test with 20 different variants and found one significant result. Would you suspect any issues with these results?

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

• Why would the number of job applicants decrease while job postings remain the same? You observe that the number of job postings per day has remained constant, 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 problems in "messy" datasets.

• Is this a fair coin? 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.

• Write a function to calculate sample variance from a list of integers. Create a function that takes a list of integers and returns the sample variance, rounded 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 the A/B test results with 20 variants? Your manager ran an A/B test with 20 different variants and found one significant result. Evaluate if there is anything suspicious about these results.

• Write a function to return the median value of a list in O(1) time and space. Given a sorted list of integers where more than 50% of the list is 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? You have data on student test scores in two different layouts. Identify the drawbacks of these layouts, suggest formatting changes for better analysis, and describe common problems 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 the advantages of using random forest over logistic regression.

• 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 for a business problem and explain its predictions to non-technical stakeholders? Your manager asks you to build a neural network model to solve 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? 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?

What is the average salary for a Data Engineer at [24]7.Ai?

Q: What makes [24]7.ai a standout company in the AI and customer experience space?

[24]7.ai uses advanced conversational AI, combined with over 20 years of contact center expertise, to deliver personalized, frictionless conversations across digital and voice channels. Recognized globally, [24]7.ai is a leader in Conversational AI, Intelligent Virtual Assistants, and CXaaS, with numerous industry accolades to its name.

Q: What are the primary responsibilities for a Data Engineer at [24]7.ai?

As a Data Engineer, you'll design, develop, and maintain scalable data pipelines using Python, Pandas, Spark, and Dataflow. You'll optimize and troubleshoot data pipelines for performance and reliability, collaborate with cross-functional teams, and ensure data integrity and quality through validation and testing strategies.

Q: What qualifications and experience are required for the Data Engineer position?

You should have a bachelor's degree in Computer Science, Information Science, or Data Science, coupled with 6 to 11 years of relevant experience in design and development. Proficiency in SQL Server, MySQL, Google Cloud Platform, and strong communication skills are essential. Knowledge of analytics tools like PowerBI, Tableau, Data Studio, and Apache SuperSet is preferred.

Q: What is the work culture like at [24]7.ai?

At [24]7.ai, you'll be part of a team passionate about leveraging AI, machine learning, and human intelligence to enhance customer interactions for leading global brands. The company is recognized for its inclusive workplace, particularly for women, and offers opportunities for continual learning and professional growth.

Q: How can I prepare for an interview for a Data Engineer position at [24]7.ai?

To prepare, research [24]7.ai’s products and services, focus on common interview topics, and polish your technical skills. Practice with Interview Query to simulate real interview scenarios and enhance your problem-solving and data engineering competencies.

If you're ready to join a team passionate about utilizing artificial intelligence, machine learning, and human insight to connect world-leading brands with their customers, then [24]7.ai is the place for you. As a Data Engineer here, you'll design, develop, and optimize scalable data pipelines, ensuring data integrity and quality while collaborating with cross-functional teams.

For more insights about the company, check out our main 24-7-ai Interview Guide, where we have covered many interview questions that could be asked. 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 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!