Ihs Data Engineer Interview Guide

Getting ready for a Data Engineer interview at IHS? The IHS Data Engineer interview process typically spans a wide range of question topics and evaluates skills in areas like data pipeline architecture, SQL and Python programming, ETL system design, data quality management, and the clear presentation of complex data solutions. Interview preparation is especially important for this role at IHS, as candidates are expected to demonstrate not only technical expertise in building robust and scalable data systems, but also the ability to communicate insights and solutions effectively to both technical and non-technical audiences in a fast-paced, data-driven environment.

In preparing for the interview, you should:

• Understand the core skills necessary for Data Engineer positions at IHS.
• Gain insights into IHS’s Data Engineer interview structure and process.
• Practice real IHS 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 IHS Data Engineer interview process, along with sample questions and preparation tips tailored to help you succeed.

IHS Markit is a global leader in information, analytics, and solutions for key industries and markets that drive economies worldwide. The company partners with clients in business, finance, and government to deliver actionable insights, enabling informed and confident decision-making. Serving over 50,000 customers in more than 140 countries—including 85% of the Fortune Global 500—IHS Markit is headquartered in London and listed on NASDAQ (INFO). As a Data Engineer, you will contribute to building and optimizing data systems that support the company’s mission of delivering critical insights for strategic growth and decision support.

As a Data Engineer at IHS, you will design, build, and maintain data pipelines that support the company’s analytics and business intelligence initiatives. You will work with large, complex datasets from various sources, ensuring data is clean, reliable, and optimized for analysis. Key responsibilities include developing ETL processes, collaborating with data scientists and analysts, and implementing solutions that enable efficient data storage and retrieval. This role is essential for powering IHS’s data-driven decision making, supporting product development, and enhancing operational efficiency across the organization.

2.1 Stage 1: Application & Resume Review

The first step in the IHS Data Engineer interview process is the application and resume review. At this stage, recruiters and technical screeners evaluate your background for relevant experience in data engineering, focusing on your proficiency with SQL, Python, data pipeline design, and experience with large-scale ETL processes. Demonstrating experience in building robust, scalable data solutions and an ability to clearly communicate technical accomplishments is crucial. To prepare, ensure your resume highlights hands-on data engineering projects, your familiarity with modern data architecture, and your ability to collaborate cross-functionally.

2.2 Stage 2: Recruiter Screen

Once your application passes the initial screening, you will participate in a recruiter screen. This is typically a brief conversation—often conducted by a talent acquisition specialist—focused on your motivation for applying to IHS, your understanding of the data engineer role, and a high-level overview of your technical background. Expect questions about your career trajectory, your interest in data engineering, and your alignment with IHS’s mission. Preparation should include a concise personal pitch, clear articulation of your career goals, and familiarity with IHS’s business domains.

2.3 Stage 3: Technical/Case/Skills Round

The technical evaluation at IHS often includes several components. You may be asked to complete a take-home assignment that tests your ability to design and implement data pipelines, perform complex SQL queries, or solve data transformation challenges. Additionally, there may be a Pymetrics test to assess cognitive and behavioral attributes relevant to data engineering. Following this, expect one or more technical interviews—conducted virtually or in-person—where you’ll solve problems related to data modeling, ETL pipeline design, data warehouse architecture, and real-world data quality issues. You may also be asked to demonstrate Python proficiency and discuss trade-offs in technology choices. To prepare, review data pipeline patterns, practice SQL and Python coding, and be ready to discuss your approach to making complex data accessible and actionable.

2.4 Stage 4: Behavioral Interview

A behavioral interview round is designed to evaluate your soft skills, teamwork, and adaptability. Interviewers may include data team leads or cross-functional partners. You’ll be asked to describe past experiences where you overcame challenges in data projects, communicated insights to non-technical stakeholders, and adapted solutions for diverse audiences. Emphasize your ability to collaborate, learn from setbacks, and present technical information in a clear, audience-tailored manner. Preparation should focus on structuring your responses with the STAR method and reflecting on key moments that showcase your growth and impact.

2.5 Stage 5: Final/Onsite Round

The final round typically brings together multiple interviewers—including engineering managers, senior data engineers, and analytics leaders—for a comprehensive assessment. This stage may include additional technical questions, system design interviews (e.g., building scalable data warehouses, streaming pipelines), and a presentation of your take-home assignment. You may be asked to walk through your solutions, justify design decisions, and respond to follow-up questions about performance, reliability, and data quality. Strong preparation involves practicing clear, confident communication, anticipating follow-up questions, and demonstrating your ability to balance technical rigor with practical business needs.

2.6 Stage 6: Offer & Negotiation

If you successfully navigate the interviews, you’ll receive a verbal or written offer from the IHS recruiting team. This final step covers compensation, benefits, and start date negotiations. The recruiter will address any outstanding questions and guide you through the onboarding process. Preparation here involves researching market compensation benchmarks and identifying your priorities to ensure a smooth negotiation.

2.7 Average Timeline

The typical IHS Data Engineer interview process spans 3-5 weeks from initial application to offer. Fast-track candidates with strong technical backgrounds may complete the process in as little as 2 weeks, while the standard pace allows a few days to a week between each stage, especially for take-home assignments and scheduling multi-panel interviews. The process is designed to thoroughly assess both technical depth and communication skills, so timely preparation for each stage is key.

Next, let’s dive into the specific types of interview questions you can expect at each stage of the IHS Data Engineer process.

3.1 Data Pipeline Design & Architecture

Expect questions on designing, scaling, and optimizing data pipelines. Focus on your ability to architect end-to-end solutions, select appropriate technologies, and ensure data integrity and reliability under real-world constraints.

3.1.1 Design a robust, scalable pipeline for uploading, parsing, storing, and reporting on customer CSV data.
Describe your approach to ingesting large CSV files, handling errors, and ensuring data consistency. Discuss how you would automate parsing, leverage batch or streaming, and set up monitoring for failures.

3.1.2 Design a data pipeline for hourly user analytics.
Outline how you would architect a pipeline for near-real-time analytics, including data ingestion, transformation, and aggregation. Emphasize your choices for scalability and fault tolerance.

3.1.3 Redesign batch ingestion to real-time streaming for financial transactions.
Discuss the transition from batch to streaming, including technology choices, latency requirements, and how to guarantee data accuracy. Address potential bottlenecks and scaling strategies.

3.1.4 Let's say that you're in charge of getting payment data into your internal data warehouse.
Explain the steps for secure, reliable ingestion of payment data, including ETL design, data validation, and error handling. Highlight compliance and audit considerations.

3.1.5 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes.
Detail your approach to data ingestion, preprocessing, storage, and serving predictions. Discuss how you would ensure scalability and maintain data quality over time.

3.2 Data Modeling & Warehousing

These questions assess your ability to design flexible, efficient data models and warehouses that serve business needs. Focus on normalization, schema design, and strategies for supporting analytics at scale.

3.2.1 Design a data warehouse for a new online retailer.
Describe your process for modeling retail data, including customer, product, and transaction tables. Discuss partitioning, indexing, and supporting business queries.

3.2.2 How would you design a data warehouse for a e-commerce company looking to expand internationally?
Explain how you would accommodate multiple currencies, languages, and regional compliance. Address scalability and cross-border reporting.

3.2.3 Design a dynamic sales dashboard to track McDonald's branch performance in real-time.
Discuss how you would model sales data, enable real-time updates, and optimize for dashboard performance. Cover data freshness and aggregation techniques.

3.2.4 System design for a digital classroom service.
Outline your approach to modeling classroom, user, and activity data. Highlight scalability, user privacy, and analytics support.

3.3 Data Quality, Cleaning & Reliability

Expect questions around identifying, diagnosing, and resolving data quality issues. Emphasize systematic approaches, automation, and communication with stakeholders.

3.3.1 How would you systematically diagnose and resolve repeated failures in a nightly data transformation pipeline?
Describe your process for monitoring, alerting, and root cause analysis. Discuss how you would implement automated recovery and communicate with impacted teams.

3.3.2 How would you approach improving the quality of airline data?
Explain your strategy for profiling, cleaning, and validating large, complex datasets. Highlight tools and frameworks for ongoing quality assurance.

3.3.3 Describing a real-world data cleaning and organization project
Share your step-by-step methodology for cleaning messy data, including profiling, handling missing values, and standardizing formats. Emphasize reproducibility and documentation.

3.3.4 Ensuring data quality within a complex ETL setup
Discuss strategies for validating data across multiple sources and transformations. Cover automated tests, anomaly detection, and stakeholder communication.

3.4 SQL & Programming Skills

You’ll be tested on your SQL and Python skills, including query optimization, data manipulation, and algorithmic problem solving. Focus on writing efficient, readable code and handling large datasets.

3.4.1 Write a SQL query to count transactions filtered by several criterias.
Clarify filters, join logic, and aggregation. Optimize for performance on large tables and discuss handling edge cases.

3.4.2 Write a function to return a new list where all empty values are replaced with the most recent non-empty value in the list.
Explain your approach to iterating through data and updating values efficiently. Consider edge cases and performance.

3.4.3 python-vs-sql
Discuss scenarios where you’d choose Python over SQL or vice versa, considering scalability, maintainability, and complexity.

3.4.4 Implement one-hot encoding algorithmically.
Outline your method for transforming categorical data into numerical format. Address efficiency and handling unseen categories.

3.4.5 Given a string, write a function to find its first recurring character.
Describe your algorithm for tracking occurrences and identifying the first repeat efficiently.

3.5 Communication & Presentation of Data Insights

These questions evaluate your ability to translate technical findings into actionable business insights for non-technical audiences. Focus on clarity, adaptability, and tailoring your message to stakeholders.

3.5.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Discuss your approach to structuring presentations, using visual aids, and adjusting technical depth for different audiences.

3.5.2 Making data-driven insights actionable for those without technical expertise
Explain how you simplify complex concepts and ensure your recommendations are clear and actionable.

3.5.3 Demystifying data for non-technical users through visualization and clear communication
Share techniques for creating intuitive dashboards and documentation that empower non-technical users.

3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision.
Focus on a specific project where your analysis led to a clear business outcome. Emphasize the impact of your recommendation and how you communicated it.

3.6.2 Describe a challenging data project and how you handled it.
Choose a project with multiple obstacles—technical, organizational, or timeline-related—and detail your problem-solving and collaboration strategies.

3.6.3 How do you handle unclear requirements or ambiguity?
Share your approach to clarifying goals, asking targeted questions, and iteratively refining solutions with stakeholders.

3.6.4 Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?
Explain how you adapted your communication style, used visualizations, or sought feedback to bridge gaps.

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?
Discuss your methodology for investigating discrepancies, validating data sources, and documenting your decision.

3.6.6 How do you prioritize multiple deadlines? Additionally, how do you stay organized when you have multiple deadlines?
Outline your system for task management, stakeholder alignment, and adjusting priorities under pressure.

3.6.7 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Describe how you identified the root cause, built automation, and monitored results to prevent future issues.

3.6.8 Tell me about a time you delivered critical insights even though 30% of the dataset had nulls. What analytical trade-offs did you make?
Explain your approach to handling missing data, including profiling, imputation, and communicating uncertainty.

3.6.9 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.
Detail how you leveraged prototypes or visualizations to clarify requirements and gain consensus.

3.6.10 Walk us through how you built a quick-and-dirty de-duplication script on an emergency timeline.
Describe your process for rapidly identifying and removing duplicates, including tools and verification steps.

4.1 Company-specific tips:

Familiarize yourself with IHS Markit’s core business domains, including finance, energy, and government analytics. Understand how data engineering supports the delivery of actionable insights to clients across these sectors. Review IHS’s approach to information management, large-scale data integration, and the importance of data reliability in mission-critical decision-making.

Research recent IHS Markit initiatives involving big data, cloud migration, and analytics platforms. Be prepared to discuss how data engineering enables strategic growth, operational efficiency, and compliance for global clients. Demonstrate your understanding of IHS’s commitment to data integrity, scalability, and the clear communication of technical solutions to both technical and non-technical audiences.

4.2 Role-specific tips:

4.2.1 Practice designing scalable, end-to-end data pipelines for diverse business scenarios.
Prepare to discuss your approach to architecting robust data pipelines for tasks such as CSV ingestion, real-time analytics, and secure payment data processing. Emphasize your ability to select appropriate technologies, automate error handling, and ensure data consistency and reliability at scale.

4.2.2 Refine your skills in ETL system design and data transformation.
Be ready to explain your process for building efficient ETL workflows, including data extraction, cleansing, transformation, and loading into data warehouses. Highlight your experience optimizing for performance, monitoring for failures, and implementing automated recovery mechanisms.

4.2.3 Strengthen your data modeling and warehousing expertise.
Review best practices for designing flexible, normalized schemas to support business intelligence and analytics. Practice modeling scenarios for retail, e-commerce, and digital services, focusing on partitioning, indexing, and supporting complex queries for large datasets.

4.2.4 Prepare to address data quality and reliability challenges.
Demonstrate your systematic approach to diagnosing and resolving data quality issues, such as repeated pipeline failures or inconsistent source metrics. Discuss strategies for profiling, cleaning, and validating large datasets, as well as automating data-quality checks to prevent future crises.

4.2.5 Sharpen your SQL and Python programming skills for data manipulation and analysis.
Practice writing efficient queries to handle large transaction tables, implement data transformation algorithms (such as one-hot encoding), and solve algorithmic problems like finding recurring characters. Be able to articulate trade-offs between Python and SQL for different data engineering tasks.

4.2.6 Focus on clear communication and presentation of data insights.
Prepare examples of how you’ve structured presentations for technical and non-technical audiences, using visual aids and dashboards to demystify complex data. Show your ability to tailor technical depth, simplify concepts, and make recommendations actionable for stakeholders.

4.2.7 Reflect on behavioral scenarios and teamwork.
Use the STAR method to prepare stories about overcoming challenges in data projects, handling ambiguity, and aligning stakeholders with different visions. Be ready to discuss how you manage deadlines, organize tasks, and adapt your communication style to bridge gaps and drive consensus.

4.2.8 Be ready to discuss real-world trade-offs in data engineering.
Share experiences where you delivered insights despite incomplete or messy data, explaining your analytical decisions, handling of missing values, and communication of uncertainty. Highlight your ability to balance technical rigor with practical business needs.

4.2.9 Demonstrate your ability to automate and optimize data processes under pressure.
Prepare examples of building quick solutions, such as emergency de-duplication scripts or automating recurrent data-quality checks. Emphasize your problem-solving skills, choice of tools, and verification steps to ensure reliability.

4.2.10 Practice explaining your technology choices and design decisions.
Anticipate follow-up questions about performance, reliability, and scalability in your solutions. Be prepared to justify your decisions, discuss trade-offs, and adapt your approach based on evolving business requirements.

5.1 How hard is the Ihs Data Engineer interview?
The Ihs Data Engineer interview is considered challenging, especially for candidates new to large-scale data engineering. You’ll be tested on your ability to design robust data pipelines, optimize ETL processes, and tackle real-world data quality issues. The interview also evaluates your SQL and Python proficiency, communication skills, and adaptability in a fast-paced, data-driven environment. Candidates with hands-on experience in building scalable data solutions and presenting insights to diverse audiences generally perform well.

5.2 How many interview rounds does Ihs have for Data Engineer?
Typically, the Ihs Data Engineer interview process includes 5-6 rounds. These comprise the initial application and resume review, recruiter screen, technical/case/skills round (which may feature a take-home assignment and cognitive assessment), behavioral interview, final onsite round with multiple interviewers, and the offer/negotiation stage.

5.3 Does Ihs ask for take-home assignments for Data Engineer?
Yes, Ihs often includes a take-home assignment in the technical evaluation stage. You may be asked to design and implement a data pipeline, solve complex SQL or Python problems, or address data transformation scenarios. The assignment is designed to assess your practical skills and your ability to communicate technical solutions clearly.

5.4 What skills are required for the Ihs Data Engineer?
Key skills for the Ihs Data Engineer role include expertise in data pipeline architecture, advanced SQL and Python programming, ETL system design, data modeling for warehousing, and data quality management. Strong communication skills are essential for presenting complex data insights to both technical and non-technical audiences. Experience with large-scale data integration, cloud platforms, and automation of data processes is highly valued.

5.5 How long does the Ihs Data Engineer hiring process take?
The typical timeline for the Ihs Data Engineer hiring process is 3-5 weeks from initial application to offer. Fast-track candidates with a strong technical background may complete the process in as little as 2 weeks, while the standard pace allows for several days between each stage, particularly for take-home assignments and multi-panel interviews.

5.6 What types of questions are asked in the Ihs Data Engineer interview?
You’ll encounter questions on data pipeline design, ETL optimization, data modeling for warehousing, SQL and Python programming, and real-world data quality challenges. Behavioral questions will assess your teamwork, adaptability, and communication skills. Expect to discuss trade-offs in technology choices, present data insights, and solve problems based on actual business scenarios.

5.7 Does Ihs give feedback after the Data Engineer interview?
Ihs typically provides high-level feedback through recruiters, especially regarding overall performance and fit. Detailed technical feedback may be limited, but candidates are encouraged to follow up for clarification and guidance on improvement areas.

5.8 What is the acceptance rate for Ihs Data Engineer applicants?
While specific acceptance rates aren’t publicly disclosed, the Ihs Data Engineer role is highly competitive. Based on industry benchmarks, the estimated acceptance rate is around 3-6% for qualified applicants who successfully navigate all interview stages.

5.9 Does Ihs hire remote Data Engineer positions?
Yes, Ihs offers remote opportunities for Data Engineers. Some roles may require occasional office visits for collaboration, but the company supports flexible work arrangements, especially for candidates with strong technical and communication skills.

• Related guides:
  • Ihs interview questions
  • Data Engineer interview guide
  • “Top data engineering interview tips”

Ready to ace your Ihs Data Engineer interview? It’s not just about knowing the technical skills—you need to think like an Ihs Data Engineer, solve problems under pressure, and connect your expertise to real business impact. That’s where Interview Query comes in with company-specific learning paths, mock interviews, and curated question banks tailored toward roles at Ihs and similar companies.

With resources like the Ihs Data Engineer Interview Guide and our latest case study practice sets, you’ll get access to real interview questions, detailed walkthroughs, and coaching support designed to boost both your technical skills and domain intuition. Dive into topics like data pipeline architecture, ETL system design, data modeling, SQL and Python programming, and strategies for communicating insights to technical and non-technical audiences—all directly relevant to succeeding at Ihs.

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