ITV Network Data Engineer interview questions & guide 2026

The interview process at ITV Network is designed to assess both your technical proficiency with modern data stacks and your ability to navigate team-based problem solving. While individual experiences vary, the following categories represent the core areas of assessment.

Technical Proficiency and Coding

These questions test your hands-on ability to manipulate data and write clean, efficient code. Expect to discuss your approach to data transformation and architectural choices.

• How would you optimize a PySpark job that is performing poorly on a large dataset?
• Can you explain your process for handling schema evolution in data pipelines?

Preparation for ITV Network should focus on demonstrating both your technical depth and your ability to work within a fast-paced, collaborative environment. You should be prepared to articulate not just "how" you solve a problem, but "why" you chose a specific tool or methodology.

Role-related Knowledge – This refers to your mastery of tools like Python, SQL, and distributed frameworks like PySpark. Interviewers look for evidence that you understand the underlying mechanics of these tools, not just how to call their APIs.

Problem-solving Ability – You will be evaluated on your logical approach to ambiguous problems. When faced with a take-home assessment or a case study, focus on documenting your assumptions, trade-offs, and testing strategies.

Communication and Leadership – ITV Network values engineers who can bridge the gap between technical implementation and business value. Be ready to discuss how you have influenced project outcomes or managed expectations with stakeholders.

The interview process at ITV Network is typically structured to be efficient, focusing on a balance of technical capability and cultural alignment. Candidates should expect a screening phase followed by a practical assessment of their skills. The process is generally fast-paced, reflecting a desire to move quickly with qualified talent, though it remains rigorous in its technical demands.

The cornerstone of the process is often a take-home programming assignment. This is designed to test your real-world coding ability, specifically regarding data transformation and handling. Subsequent stages typically involve a review of this assignment with members of the engineering team, followed by a behavioral or "culture" interview to ensure you will be a successful addition to the team.

The visual timeline above outlines the typical progression from initial contact to final decision. Use this to structure your study time, ensuring you are prepared for both the deep-dive technical reviews of your code and the broader discussions regarding your professional experience and values.

Technical Assessment

This area is critical and usually centers on your take-home assignment. You will be evaluated on code quality, performance, and your ability to handle edge cases in data processing.

Be ready to go over:

• Data Transformation Logic – Explain why you chose specific libraries or functions.
• Error Handling – Demonstrate how your code manages corrupt or missing data.

As a Data Engineer, your primary objective is to build and maintain the infrastructure that turns raw data into intelligence. You will spend a significant portion of your time writing and optimizing data pipelines that process high-velocity data. This involves ensuring that data is accurately ingested, transformed, and loaded into data warehouses or lakes where it can be consumed by analysts and scientists.

Collaboration is essential. You will regularly interface with product teams to understand their data needs and with software engineers to ensure that the data being emitted by applications is clean and usable. You will also be responsible for maintaining the health of these pipelines, which includes proactive monitoring, troubleshooting performance bottlenecks, and performing system upgrades.

A strong candidate for this role possesses a deep technical foundation and a pragmatic approach to engineering.

• Must-have skills: Proficient in Python and SQL; experience with distributed computing (e.g., PySpark); understanding of ETL/ELT patterns; experience with cloud-based data platforms.
• Nice-to-have skills: Exposure to Airflow or similar orchestration tools; experience with CI/CD for data pipelines; familiarity with data modeling for analytics.
• Soft skills: Ability to communicate technical trade-offs to non-technical stakeholders; comfort working in an agile, cross-functional team; a proactive mindset toward identifying and solving data quality issues.

Q: How long should I spend on the take-home assignment? A: While the guidance provided is typically 4–5 hours, ensure you prioritize code quality and testability over adding excessive, unrequested features.

Q: What is the primary focus of the technical interviews? A: The technical interviews are heavily focused on your submitted work; be prepared to defend your architectural decisions and explain how you would scale your solution.

Q: Is the culture at ITV Network collaborative? A: Yes, the team environment is highly emphasized. You should be prepared to discuss how you contribute to a team and how you handle technical disagreements.

Q: What is the typical timeline for the process? A: The process can be quite efficient, with some candidates receiving an offer within two weeks of initial contact, though this can vary based on team availability.

• Prepare for code review: Treat your take-home assignment as a production pull request. Ensure it is well-documented and follows best practices.
• Focus on the "Why": When explaining your solutions, always articulate the trade-offs you considered. This demonstrates senior-level thinking.
• Know your own resume: Be prepared to speak in detail about every project you list, specifically focusing on the data challenges you faced and how you overcame them.
• Ask meaningful questions: Use the interview to learn about the team’s current data challenges—it shows you are already thinking about how to contribute.

The Data Engineer role at ITV Network offers a unique opportunity to shape the data landscape of a major media organization. By focusing on your technical fundamentals, being prepared to discuss your past work in detail, and demonstrating a collaborative mindset, you will be well-positioned to succeed in your interviews. Remember that the interviewers are looking for a teammate who is both technically capable and easy to work with.

You can explore additional interview insights, practice questions, and preparation resources on Dataford to further refine your approach. With dedicated preparation and a clear focus on the evaluation areas outlined in this guide, you can confidently navigate the process.

The compensation data above provides an overview of the typical salary ranges for this position. Interpret these figures as a market-based baseline, keeping in mind that total compensation may include additional benefits, bonuses, or stock options depending on the seniority and specific team requirements.