IMG Data Engineer Interview Guide

Getting ready for a Data Engineer interview at IMG? The IMG Data Engineer interview process typically spans multiple question topics and evaluates skills in areas like data pipeline design, SQL and Python proficiency, data modeling, ETL processes, and communicating technical insights to both technical and non-technical stakeholders. Interview preparation is especially important for this role at IMG because candidates are expected to demonstrate hands-on expertise in building robust data systems, ensuring data quality, and translating complex data challenges into actionable solutions that align with IMG’s high standards for customer satisfaction and operational excellence.

In preparing for the interview, you should:

• Understand the core skills necessary for Data Engineer positions at IMG.
• Gain insights into IMG’s Data Engineer interview structure and process.
• Practice real IMG Data Engineer interview questions to sharpen your performance.

At Interview Query, we regularly analyze interview experience data shared by candidates. This guide uses that data to provide an overview of the IMG Data Engineer interview process, along with sample questions and preparation tips tailored to help you succeed.

Founded in 1987, IMG is a leading small business specializing in professional information technology services, with a strong reputation for competence, integrity, and customer satisfaction. Serving clients for over 35 years, IMG delivers customer-focused IT solutions across technical services and operations, emphasizing long-term partnerships and professional excellence. The company values its people as the foundation of its success and is committed to fostering a dynamic and inclusive work environment. As a Data Engineer at IMG, you will contribute to the design, development, and management of data systems that support the company’s mission of exceeding client expectations through innovative technology solutions.

As a Data Engineer at IMG, you will work collaboratively within a team to design, develop, test, document, deploy, and manage robust data systems that support the company’s technical operations. Your responsibilities will include data modeling, building data architectures, and developing data integration and ETL processes using tools like Talend or Apache NiFi. You will leverage your expertise in SQL databases and Python to create effective data applications, and may also utilize data visualization tools such as Tableau or Power BI to present insights. This role is essential to ensuring data integrity, accessibility, and reliability, supporting IMG’s mission to deliver high-quality IT and professional services to its clients.

2.1 Stage 1: Application & Resume Review

At IMG, the initial stage involves a thorough review of your resume and application materials by the recruiting team, focusing on your experience in data engineering, data modeling, SQL database proficiency (such as Oracle, PostgreSQL, or MySQL), and Python programming. Candidates with direct experience building data applications, designing data systems, and working with ETL tools are prioritized. Ensure your resume clearly demonstrates your technical expertise and highlights complex data challenges you have tackled, as well as your ability to collaborate in dynamic, team-driven environments.

2.2 Stage 2: Recruiter Screen

The recruiter screen is typically a 30-minute phone or video call conducted by a member of IMG’s talent acquisition team. This conversation centers on your motivation for joining IMG, your background in data engineering, and your communication skills. Expect to discuss your professional journey, why you are interested in IMG, and your ability to adapt to fast-paced, customer-focused environments. Preparation should focus on articulating your career progression, your alignment with IMG’s values, and your ability to work collaboratively.

2.3 Stage 3: Technical/Case/Skills Round

This stage generally consists of one or two interviews, led by a data team hiring manager or senior engineers. You’ll be assessed on technical skills such as SQL query writing, Python programming (including packages like Pandas and NumPy), data modeling, and data architecture. Expect case-based questions related to building robust ETL pipelines, designing scalable data systems, integrating data sources, and troubleshooting transformation failures. You may also be asked to solve problems involving large-scale data processing, data cleaning, and system design for applications such as real-time transaction streaming or ingestion pipelines. Preparation should include practicing hands-on technical challenges, reviewing recent data projects, and being ready to discuss your approach to designing and optimizing data solutions.

2.4 Stage 4: Behavioral Interview

The behavioral interview is typically conducted by a data team lead or project manager. This round explores your ability to communicate complex data insights to technical and non-technical audiences, your experience working within agile teams, and your strategies for overcoming hurdles in data projects. You’ll be expected to share examples of how you’ve collaborated cross-functionally, resolved project challenges, and adapted your communication style for different stakeholders. Prepare by reflecting on your teamwork experiences, how you’ve handled setbacks, and your methods for ensuring data quality and project success.

2.5 Stage 5: Final/Onsite Round

The final stage often consists of a virtual or onsite panel interview, which may include presentations, technical deep-dives, and scenario-based discussions. The panel typically includes senior data engineers, analytics directors, and sometimes business stakeholders. You may be asked to present a previous data project, walk through architectural decisions, or respond to system design prompts involving complex pipelines, data warehouse solutions, or scalable reporting systems. This stage emphasizes both technical mastery and the ability to communicate insights clearly and adaptively. Preparation should focus on rehearsing presentations, reviewing your portfolio, and anticipating follow-up questions about your design choices and project impact.

2.6 Stage 6: Offer & Negotiation

Once you successfully complete all rounds, IMG’s recruiter will reach out to discuss the offer, compensation, benefits, and onboarding timeline. This stage is typically a one-on-one conversation, where you can clarify role expectations, negotiate terms, and confirm your fit within the team. Preparation should include researching industry standards for data engineering compensation and benefits, as well as preparing thoughtful questions about professional development and growth opportunities at IMG.

2.7 Average Timeline

The IMG Data Engineer interview process usually spans 3–5 weeks from initial application to final offer. Fast-track candidates with highly relevant experience and strong technical backgrounds may progress in as little as 2–3 weeks, while the standard pace allows roughly a week between each stage to accommodate scheduling and panel availability. Technical rounds may be scheduled consecutively or spaced out, depending on team bandwidth and candidate availability.

Next, let’s explore the specific interview questions you may encounter throughout the IMG Data Engineer interview process.

3.1 Data Pipeline Design & ETL

Data engineers at IMG are expected to design, optimize, and maintain robust data pipelines that scale across diverse data sources and business needs. You’ll be assessed on your ability to architect ETL solutions, troubleshoot failures, and ensure data quality and reliability in production environments.

3.1.1 Design a robust, scalable pipeline for uploading, parsing, storing, and reporting on customer CSV data
Explain your approach to building a modular pipeline, including error handling, schema validation, and monitoring. Discuss how you’d ensure scalability and maintainability for ongoing ingestion.

3.1.2 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes
Describe how you’d collect, clean, and transform raw data, then serve it for analytics or machine learning. Highlight your choices for scheduling, storage, and serving layers.

3.1.3 Design a solution to store and query raw data from Kafka on a daily basis
Discuss how you’d ingest streaming data, partition storage, and make data available for downstream analysis. Address scalability, retention policies, and query performance.

3.1.4 How would you systematically diagnose and resolve repeated failures in a nightly data transformation pipeline?
Outline your troubleshooting process, including logging, alerting, and root cause analysis. Suggest strategies for improving pipeline reliability and preventing future issues.

3.1.5 Aggregating and collecting unstructured data
Explain how you’d handle ingestion, parsing, and transformation of unstructured data sources. Focus on tools, schema evolution, and data enrichment techniques.

3.2 Data Modeling & Warehousing

IMG values engineers who can design scalable, flexible data models and architect efficient data warehouses to support analytics and reporting. Expect questions about schema design, normalization, and storage strategies.

3.2.1 Design a data warehouse for a new online retailer
Describe your approach to modeling core entities, handling slowly changing dimensions, and optimizing for query performance.

3.2.2 Design a reporting pipeline for a major tech company using only open-source tools under strict budget constraints
Discuss your selection of open-source ETL, storage, and visualization tools, and how you’d architect the system for reliability and cost-effectiveness.

3.2.3 Design a data pipeline for hourly user analytics
Explain how you’d aggregate and store user activity data, ensuring timely and accurate reporting.

3.2.4 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners
Highlight how you’d handle schema variability, data validation, and partner onboarding.

3.2.5 Ensuring data quality within a complex ETL setup
Describe your approach to monitoring, testing, and remediating data quality issues in multi-source ETL environments.

3.3 SQL & Data Manipulation

You’ll need to demonstrate advanced SQL skills to manipulate, aggregate, and analyze large datasets efficiently. Expect questions that test your ability to write performant queries and handle real-world data scenarios.

3.3.1 Write a SQL query to find the average number of right swipes for different ranking algorithms
Summarize how you’d group data by algorithm, calculate averages, and optimize for large datasets.

3.3.2 Write a query to compute the average time it takes for each user to respond to the previous system message
Explain how you’d use window functions or self-joins to align events and calculate response times.

3.3.3 Modifying a billion rows
Describe strategies for safely updating or transforming massive tables, minimizing downtime and performance impact.

3.3.4 Select the 2nd highest salary in the engineering department
Discuss how you’d use ranking functions or subqueries to efficiently find the desired result.

3.3.5 Write a query to get the largest salary of any employee by department
Explain your approach to grouping and aggregation, and how you’d optimize for speed.

3.4 System Design & Scalability

IMG expects data engineers to design systems that scale seamlessly and support business growth. You’ll be asked to architect solutions for high-throughput, low-latency, and real-time data scenarios.

3.4.1 Redesign batch ingestion to real-time streaming for financial transactions
Describe the migration process, technology choices, and challenges in ensuring data consistency and reliability.

3.4.2 How would you design database indexing for efficient metadata queries when storing large Blobs?
Explain your indexing strategy, trade-offs, and how you’d optimize for query speed and storage efficiency.

3.4.3 System design for real-time tweet partitioning by hashtag at Apple
Discuss your approach to partitioning, scaling, and ensuring high availability in real-time environments.

3.4.4 Design the system supporting an application for a parking system
Outline how you’d handle data ingestion, state management, and user queries at scale.

3.4.5 Estimate the cost of storing Google Earth photos each year
Share your approach to storage estimation, cost modeling, and optimization strategies.

3.5 Data Quality & Cleaning

Maintaining high data quality is essential for reliable analytics and machine learning. You’ll be evaluated on your experience with cleaning, profiling, and organizing large, messy datasets.

3.5.1 Describing a real-world data cleaning and organization project
Summarize your process for identifying issues, cleaning data, and documenting steps for reproducibility.

3.5.2 Challenges of specific student test score layouts, recommended formatting changes for enhanced analysis, and common issues found in "messy" datasets
Discuss how you’d handle inconsistent formats, missing values, and prepare the data for analysis.

3.5.3 How would you approach improving the quality of airline data?
Describe the steps you’d take to audit, clean, and monitor data quality over time.

3.5.4 Demystifying data for non-technical users through visualization and clear communication
Explain how you’d make complex datasets accessible and actionable for stakeholders.

3.5.5 Making data-driven insights actionable for those without technical expertise
Share strategies for translating technical findings into clear, business-relevant recommendations.

3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision that impacted business outcomes.
Describe the context, the data you analyzed, and how your insights led to a measurable result. Focus on your business understanding and communication.

3.6.2 Describe a challenging data project and how you handled it.
Summarize the technical and organizational hurdles you faced, your problem-solving approach, and the outcome.

3.6.3 How do you handle unclear requirements or ambiguity in project scope?
Discuss your process for clarifying goals, communicating with stakeholders, and iterating on solutions.

3.6.4 Walk us through how you built a quick-and-dirty de-duplication script on an emergency timeline.
Explain your prioritization, technical choices, and how you balanced speed with reliability.

3.6.5 Describe a situation where two source systems reported different values for the same metric. How did you decide which one to trust?
Detail your validation process, stakeholder engagement, and resolution strategy.

3.6.6 How have you balanced speed versus rigor when leadership needed a “directional” answer by tomorrow?
Share your triage approach, how you communicated uncertainty, and the trade-offs you made.

3.6.7 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Describe the tools or scripts you built, and the impact on team efficiency and data reliability.

3.6.8 Tell us about a time you delivered critical insights even though 30% of the dataset had nulls. What analytical trade-offs did you make?
Explain your missing data treatment, how you communicated limitations, and the business value delivered.

3.6.9 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.
Discuss how you facilitated collaboration, iterated on feedback, and drove consensus.

3.6.10 Describe a time you had to negotiate scope creep when two departments kept adding “just one more” request. How did you keep the project on track?
Explain your prioritization framework, communication strategy, and how you maintained data integrity and team trust.

4.1 Company-specific tips:

Familiarize yourself with IMG’s history, mission, and client-focused approach. Understand the company’s emphasis on long-term partnerships, technical excellence, and delivering innovative IT solutions. Be ready to articulate how your work as a data engineer aligns with IMG’s values of integrity, competence, and customer satisfaction.

Research IMG’s core business areas, including their technical services and operations. Prepare to discuss how robust data engineering can drive value for IMG’s clients and support operational excellence. Show that you appreciate the importance of data reliability and accessibility in the context of professional services.

Highlight your ability to thrive in dynamic, team-oriented environments. IMG values collaboration and inclusivity, so prepare examples that showcase your experience working cross-functionally and adapting to evolving business needs. Demonstrate your commitment to continuous learning and professional growth, which aligns with IMG’s people-first culture.

4.2 Role-specific tips:

4.2.1 Master the end-to-end design of scalable data pipelines and ETL solutions.
Be ready to discuss your experience architecting modular pipelines for ingesting, parsing, storing, and reporting on diverse data sources. Practice explaining how you ensure pipeline scalability, error handling, schema validation, and monitoring, especially when dealing with heterogeneous or unstructured data. Highlight your proficiency with ETL tools such as Talend or Apache NiFi, and your approach to troubleshooting repeated failures in nightly transformation jobs.

4.2.2 Demonstrate advanced SQL and Python proficiency for real-world data manipulation.
Prepare to showcase your ability to write efficient SQL queries involving complex joins, aggregations, and window functions. Be comfortable discussing strategies for safely modifying massive tables, optimizing queries for performance, and handling large-scale data transformations. In Python, emphasize your experience using libraries like Pandas and NumPy for data cleaning, wrangling, and analysis.

4.2.3 Articulate your approach to data modeling and warehousing.
Expect questions on designing scalable, flexible data models and architecting efficient data warehouses. Be prepared to discuss schema design, normalization, handling slowly changing dimensions, and optimizing for query performance. Show your awareness of open-source tools and cost-effective solutions for reporting pipelines, especially under budget constraints.

4.2.4 Highlight your skills in system design and scalability.
IMG will assess your ability to migrate batch processes to real-time streaming, design robust indexing strategies for metadata queries, and partition data for high-throughput applications. Practice explaining your choices in technology stack, data partitioning, and ensuring high availability and reliability in production systems.

4.2.5 Showcase your commitment to data quality and cleaning.
Prepare examples of projects where you identified and resolved data quality issues, cleaned messy datasets, and documented reproducible workflows. Discuss your strategies for auditing, monitoring, and automating data-quality checks to prevent recurring issues. Be ready to explain how you handle inconsistent formats, missing values, and make complex data accessible for non-technical stakeholders.

4.2.6 Communicate technical insights to both technical and non-technical audiences.
IMG values data engineers who can bridge the gap between data and business. Practice translating complex findings into clear, actionable recommendations. Prepare stories where you used visualizations, data prototypes, or wireframes to align stakeholders and drive consensus on project deliverables.

4.2.7 Demonstrate adaptability and problem-solving in ambiguous situations.
Reflect on experiences where you handled unclear requirements, scope creep, or conflicting data sources. Be ready to discuss your process for clarifying goals, prioritizing requests, and balancing speed with rigor when delivering “directional” answers under tight timelines. Show your ability to negotiate, communicate uncertainty, and maintain project momentum.

4.2.8 Prepare to discuss your impact through real-world business outcomes.
Have examples ready where your data engineering work led to measurable improvements in business processes, decision-making, or client satisfaction. Focus on your business understanding, ability to deliver critical insights even with imperfect data, and your role in driving operational excellence at scale.

5.1 How hard is the IMG Data Engineer interview?
The IMG Data Engineer interview is challenging, particularly for candidates who haven’t previously built production-grade data pipelines or managed complex ETL processes. The process tests your practical skills in SQL, Python, data modeling, and system design, as well as your ability to communicate technical concepts to diverse stakeholders. Candidates with hands-on experience in designing scalable data systems and troubleshooting real-world data issues will find the interview demanding but fair. IMG values both technical mastery and collaborative problem-solving, so prepare to showcase your impact across technical and business domains.

5.2 How many interview rounds does IMG have for Data Engineer?
IMG typically conducts 5–6 interview rounds for Data Engineer positions. The process begins with a recruiter screen, followed by one or two technical/case rounds, a behavioral interview, and a final onsite or virtual panel interview. Each round is designed to assess different aspects of your expertise, including technical skills, system design, communication, and cultural fit.

5.3 Does IMG ask for take-home assignments for Data Engineer?
Take-home assignments are occasionally part of the IMG Data Engineer process, especially for candidates who need to demonstrate practical skills in data pipeline design, data cleaning, or ETL troubleshooting. These assignments usually focus on real-world scenarios, such as designing a scalable ingestion pipeline or handling unstructured data, and are meant to evaluate your problem-solving approach and technical depth.

5.4 What skills are required for the IMG Data Engineer?
Key skills for an IMG Data Engineer include advanced SQL and Python proficiency, expertise in building and optimizing ETL pipelines, data modeling, and experience with data architecture. Familiarity with tools like Talend, Apache NiFi, Tableau, or Power BI is beneficial. Strong communication skills, the ability to collaborate across teams, and a commitment to data quality and reliability are essential. IMG also values adaptability, business acumen, and the ability to translate complex technical insights into actionable solutions for clients.

5.5 How long does the IMG Data Engineer hiring process take?
The IMG Data Engineer hiring process usually takes 3–5 weeks from initial application to final offer. Fast-track candidates with highly relevant experience may complete the process in as little as 2–3 weeks, while the standard pace allows for scheduling flexibility between rounds. Timelines may vary based on candidate and panel availability.

5.6 What types of questions are asked in the IMG Data Engineer interview?
Expect technical questions on data pipeline design, SQL query writing, Python programming, data modeling, and system design for scalability. You’ll also face case studies involving ETL troubleshooting, data cleaning, and real-time data processing. Behavioral questions will probe your teamwork, communication style, and approach to ambiguous requirements or scope changes. Scenario-based discussions and presentations may be included in the final panel round.

5.7 Does IMG give feedback after the Data Engineer interview?
IMG typically provides feedback through the recruiter, especially at earlier stages. While detailed technical feedback may be limited, you’ll usually receive insights into your performance and next steps. If you reach the final round, feedback may include strengths and areas for growth, helping you understand your fit for future opportunities at IMG.

5.8 What is the acceptance rate for IMG Data Engineer applicants?
While IMG does not publicly share specific acceptance rates, the Data Engineer role is highly competitive, with an estimated 5–8% acceptance rate for qualified applicants. Candidates who demonstrate strong technical skills, business understanding, and alignment with IMG’s values have the best chance of receiving an offer.

5.9 Does IMG hire remote Data Engineer positions?
Yes, IMG offers remote Data Engineer positions, with some roles requiring occasional onsite visits for team collaboration or client meetings. The company values flexibility and inclusivity, so remote work is supported for engineers who can maintain high standards of communication and productivity.

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  • Data Engineer interview guide
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