Ipsos Data Engineer Interview Questions + Guide in 2025

Ipsos is one of the world's largest research companies, renowned for its commitment to providing insightful data solutions that help clients understand market dynamics.

As a Data Engineer at Ipsos, you will play a pivotal role in enhancing the company's data infrastructure and ensuring the efficient processing and analysis of large datasets. Your primary responsibilities will include designing, coding, and testing cloud-based data pipelines, as well as developing and maintaining data warehouses that optimize storage and retrieval of large-scale datasets. You will leverage tools like SQL and Python to ingest and assemble data from various sources, working collaboratively with internal and external stakeholders to identify project goals and requirements.

The ideal candidate will possess strong technical communication skills and have a keen eye for detail, with proven experience in building data pipelines on cloud platforms such as AWS or Google Cloud Services. A solid understanding of algorithms, data manipulation, and experience with collaboration and version control tools will be essential. In addition, you should be a self-starter who thrives in a collaborative environment and is comfortable troubleshooting and refining existing infrastructure.

This guide aims to equip you with insights into the specific skills and competencies required for the Data Engineer role at Ipsos, allowing you to confidently demonstrate your qualifications and fit for the position during your interview.

The interview process for a Data Engineer at Ipsos is structured to assess both technical skills and cultural fit within the organization. It typically consists of several rounds, each designed to evaluate different competencies relevant to the role.

1. Initial Screening

The process begins with an initial screening, usually conducted by an HR representative. This is a brief conversation where the recruiter will discuss your background, the job requirements, and your interest in the position. They will also assess your communication skills and gauge your fit within Ipsos' culture.

2. Technical Assessment

Following the initial screening, candidates are often required to complete a technical assessment. This may include a written test or an online coding challenge that focuses on SQL and Python, as well as data manipulation tasks. The assessment is designed to evaluate your proficiency in handling large datasets, writing complex queries, and your understanding of algorithms and data structures.

3. Case Study or Practical Exercise

Candidates who perform well in the technical assessment may be invited to participate in a case study or practical exercise. This step involves designing a data pipeline or solving a real-world data engineering problem. You may be asked to present your approach and findings to the interviewers, demonstrating your problem-solving skills and technical expertise.

4. Panel Interviews

The next stage typically consists of one or more panel interviews with team members and management. These interviews will cover both technical and behavioral questions. Expect to discuss your previous projects, your experience with cloud services (like AWS or Google Cloud), and your approach to teamwork and collaboration. Interviewers will also assess your ability to communicate complex technical concepts clearly.

5. Final Interview

The final interview is often with senior management or the hiring director. This round may focus on your long-term career goals, your motivation for applying to Ipsos, and how you handle challenges in a team environment. It’s also an opportunity for you to ask questions about the company culture and expectations.

Throughout the process, candidates should be prepared to demonstrate their technical skills, problem-solving abilities, and how they align with Ipsos' values and mission.

Next, let’s explore the specific interview questions that candidates have encountered during this process.

Here are some tips to help you excel in your interview.

Understand the Interview Structure

The interview process at Ipsos typically involves multiple rounds, including an initial HR screening, technical assessments, and interviews with team members or management. Familiarize yourself with this structure and prepare accordingly. Expect to discuss your previous experience, technical skills, and how you can contribute to the team. Being aware of the process will help you feel more confident and prepared.

Master SQL and Python

Given the emphasis on SQL and Python in the role, ensure you are well-versed in these languages. Be prepared to answer questions that require you to write SQL queries, manipulate data, and solve real-world problems using Python. Practice common SQL operations such as joins, subqueries, and data cleaning techniques. Additionally, brush up on Python libraries relevant to data engineering, such as Pandas and NumPy.

Showcase Your Problem-Solving Skills

Ipsos values candidates who can demonstrate strong problem-solving abilities. Be ready to discuss specific examples from your past experiences where you successfully tackled complex data challenges. Use the STAR (Situation, Task, Action, Result) method to structure your responses, highlighting your analytical thinking and the impact of your solutions.

Prepare for Behavioral Questions

Expect behavioral questions that assess your teamwork, communication, and adaptability. Questions may include scenarios about handling stress, working with clients, or managing multiple projects. Reflect on your past experiences and prepare anecdotes that showcase your strengths and how you align with Ipsos' collaborative culture.

Highlight Your Experience with Cloud Services

Since the role involves working with cloud-based platforms like AWS and Google Cloud, be prepared to discuss your experience with these technologies. Share specific projects where you designed or maintained data pipelines in a cloud environment. If you have experience with serverless architectures, make sure to mention that as well.

Emphasize Attention to Detail

Attention to detail is crucial in data engineering. Be prepared to discuss how you ensure accuracy in your work, whether through code reviews, testing, or data validation processes. Providing examples of how your meticulous approach has led to successful outcomes will resonate well with the interviewers.

Communicate Effectively

Strong communication skills are essential for this role, especially when collaborating with stakeholders. Practice articulating complex technical concepts in a clear and concise manner. Be ready to explain your thought process during technical assessments and interviews, as this will demonstrate your ability to communicate effectively within a team.

Be Yourself and Stay Positive

Lastly, remember to be yourself during the interview. Ipsos values authenticity and a positive attitude. Approach the interview as a conversation rather than an interrogation. Show enthusiasm for the role and the company, and don’t hesitate to ask questions about the team and projects you would be working on.

By following these tips, you will be well-prepared to make a strong impression during your interview at Ipsos. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Ipsos. The interview process will likely focus on your technical skills, problem-solving abilities, and experience with data management and engineering. Be prepared to discuss your knowledge of SQL, Python, cloud services, and your approach to data pipeline design and optimization.

SQL and Data Manipulation

1. Can you explain the different types of joins in SQL and provide examples of when to use each?

Understanding joins is crucial for data manipulation and retrieval.

How to Answer

Discuss the various types of joins (INNER, LEFT, RIGHT, FULL) and provide scenarios where each would be applicable.

Example

“INNER JOIN is used when you want to retrieve records that have matching values in both tables. For instance, if I have a table of customers and a table of orders, I would use INNER JOIN to find customers who have placed orders. LEFT JOIN would be useful to find all customers, regardless of whether they have placed an order.”

2. How would you optimize a slow-running SQL query?

Performance optimization is key in data engineering.

How to Answer

Talk about indexing, query restructuring, and analyzing execution plans to identify bottlenecks.

Example

“To optimize a slow-running query, I would first analyze the execution plan to identify any bottlenecks. Then, I might add indexes to columns that are frequently used in WHERE clauses or JOIN conditions. Additionally, restructuring the query to reduce complexity can also improve performance.”

3. Describe a scenario where you had to clean and prepare a dataset for analysis.

Data cleaning is a critical part of data engineering.

How to Answer

Explain the steps you took to clean the data, including handling missing values and outliers.

Example

“In a recent project, I worked with a dataset that had numerous missing values and duplicates. I used SQL to identify and remove duplicates, and for missing values, I either filled them with the mean or median, depending on the data distribution, or removed the rows if they were not significant.”

4. What are window functions in SQL, and how have you used them?

Window functions are powerful for performing calculations across a set of rows.

How to Answer

Define window functions and provide an example of their application.

Example

“Window functions allow you to perform calculations across a set of rows related to the current row. For instance, I used the ROW_NUMBER() function to assign a unique sequential integer to rows within a partition of a result set, which helped in ranking sales data by region.”

5. How do you handle data integrity issues in your SQL databases?

Data integrity is essential for reliable data analysis.

How to Answer

Discuss constraints, validation rules, and regular audits.

Example

“I ensure data integrity by implementing primary and foreign key constraints in my SQL databases. Additionally, I perform regular audits and validations to check for anomalies or inconsistencies in the data.”

Python and Data Processing

1. How do you use Python for data manipulation and analysis?

Python is a key tool for data engineers.

How to Answer

Mention libraries like Pandas and NumPy and how you use them for data tasks.

Example

“I frequently use Pandas for data manipulation, such as filtering, grouping, and aggregating data. For instance, I used Pandas to clean a large dataset by removing null values and transforming data types to ensure consistency.”

2. Can you explain how you would implement a data pipeline using Python?

Data pipelines are central to data engineering.

How to Answer

Outline the steps involved in building a data pipeline, including data ingestion, processing, and storage.

Example

“To implement a data pipeline in Python, I would use libraries like Airflow for orchestration. The pipeline would start with data ingestion from various sources, followed by processing using Pandas or Dask for large datasets, and finally storing the processed data in a SQL database or a data warehouse.”

3. What is your experience with cloud services like AWS or Google Cloud?

Cloud services are essential for modern data engineering.

How to Answer

Discuss specific services you have used, such as S3, Redshift, or BigQuery.

Example

“I have experience using AWS S3 for data storage and AWS Lambda for serverless data processing. I also utilized Redshift for data warehousing, where I designed ETL processes to move data from S3 into Redshift for analysis.”

4. Describe a project where you had to troubleshoot a data pipeline issue.

Troubleshooting is a vital skill for data engineers.

How to Answer

Explain the problem, your approach to diagnosing it, and the solution you implemented.

Example

“In a previous project, our data pipeline was failing due to a schema mismatch. I diagnosed the issue by checking the logs and realized that the incoming data format had changed. I updated the data transformation scripts to accommodate the new schema, which resolved the issue.”

5. How do you ensure the scalability of your data solutions?

Scalability is crucial for handling growing datasets.

How to Answer

Discuss strategies like modular design, cloud services, and load balancing.

Example

“I ensure scalability by designing modular data pipelines that can be easily expanded. I also leverage cloud services like AWS, which allow for dynamic scaling based on demand. For instance, I implemented auto-scaling for our data processing instances to handle peak loads efficiently.”

Behavioral and Situational Questions

1. How do you prioritize your tasks when working on multiple projects?

Time management is key in a data engineering role.

How to Answer

Discuss your approach to prioritization and time management.

Example

“I prioritize tasks based on deadlines and project impact. I use project management tools to keep track of my tasks and regularly communicate with stakeholders to ensure alignment on priorities.”

2. Describe a time when you had to work collaboratively with a team.

Collaboration is essential in data engineering.

How to Answer

Provide an example of a successful team project and your role in it.

Example

“I worked on a project where we had to integrate data from multiple sources. I collaborated with data analysts and software engineers to design the data pipeline. Regular meetings helped us stay aligned, and we successfully delivered the project ahead of schedule.”

3. How do you handle stress and tight deadlines?

Handling stress is important in a fast-paced environment.

How to Answer

Discuss your strategies for managing stress and meeting deadlines.

Example

“I handle stress by staying organized and breaking tasks into manageable parts. When facing tight deadlines, I prioritize the most critical tasks and communicate with my team to ensure we are all on the same page.”

4. Can you give an example of a challenging problem you solved?

Problem-solving is a key skill for data engineers.

How to Answer

Describe the problem, your approach, and the outcome.

Example

“I faced a challenge when our data ingestion process was taking too long. I analyzed the bottlenecks and discovered that we were processing data sequentially. I restructured the pipeline to process data in parallel, which reduced the ingestion time by 50%.”

5. Why do you want to work for Ipsos?

Understanding the company’s mission and values is important.

How to Answer

Express your interest in the company’s work and how it aligns with your career goals.

Example

“I am drawn to Ipsos because of its commitment to data-driven insights and its global impact. I believe my skills in data engineering can contribute to the innovative solutions Ipsos provides to its clients, and I am excited about the opportunity to work in a collaborative environment.”