Ihs global Data Analyst Interview Guide

Getting ready for a Data Analyst interview at IHS Global? The IHS Global Data Analyst interview process typically spans 4–6 question topics and evaluates skills in areas like data cleaning and organization, pipeline design, stakeholder communication, and statistical analysis. Interview preparation is especially important for this role at IHS Global, as candidates are expected to demonstrate their ability to translate complex datasets into actionable business insights, present findings to both technical and non-technical audiences, and contribute to robust data-driven decision-making across diverse projects.

In preparing for the interview, you should:

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

IHS Global, part of S&P Global, is a leading provider of critical information, analytics, and solutions for major industries and markets worldwide. The company delivers data-driven insights to clients in sectors such as energy, finance, transportation, and technology, enabling informed decision-making and strategic planning. As a Data Analyst at IHS Global, you will play a key role in transforming complex data into actionable intelligence that supports clients’ business objectives and advances the company’s reputation for analytical excellence.

As a Data Analyst at IHS Global, you will be responsible for gathering, processing, and interpreting complex datasets to support business decisions and client projects. You will collaborate with cross-functional teams to identify trends, create visualizations, and develop actionable insights that inform market analysis and strategic planning. Typical duties include building reports, maintaining data quality, and presenting analytical findings to both internal stakeholders and external clients. This role is essential for driving data-driven solutions and supporting IHS Global’s mission of delivering valuable market intelligence and analysis across various industries.

2.1 Stage 1: Application & Resume Review

The initial step involves a careful review of your application materials, focusing on your experience with data analysis, SQL, data pipeline design, and your ability to communicate analytical findings to both technical and non-technical audiences. The hiring team looks for evidence of hands-on experience with data cleaning, ETL processes, and building dashboards or reports that inform business decisions. Highlighting your work with large datasets, data warehousing, and stakeholder communication in your resume will increase your chances of progressing to the next stage.

2.2 Stage 2: Recruiter Screen

This round is typically a 20–30 minute phone or video call with a recruiter. You can expect questions about your background, motivation for joining IHS Global, and your interest in the data analyst role. The recruiter will assess your communication skills, your understanding of the company’s mission, and your ability to explain your experience in business intelligence, data visualization, and cross-functional collaboration. Preparation should involve succinctly articulating your career trajectory and your reasons for pursuing this opportunity.

2.3 Stage 3: Technical/Case/Skills Round

In this stage, you will participate in one or more technical interviews, which may include live SQL exercises, case studies on data pipeline or warehouse design, and scenario-based questions on data cleaning, aggregation, and analysis. Interviewers may present you with real-world business problems, such as analyzing store performance, designing a scalable data ingestion pipeline, or evaluating the impact of a business initiative through A/B testing. You should be prepared to demonstrate your approach to data modeling, statistical analysis, and the synthesis of insights from multiple data sources. Practicing how to break down complex problems and communicate your thought process clearly is key.

2.4 Stage 4: Behavioral Interview

This round will focus on your ability to work within teams, resolve stakeholder misalignments, and communicate insights to both technical and non-technical audiences. Interviewers may ask you to describe challenges you’ve faced in past data projects, how you ensured data quality, and how you handled ambiguous or incomplete data. Expect to discuss your strengths and weaknesses, your approach to stakeholder communication, and examples of making data-driven insights accessible to diverse audiences. Preparation should center on specific examples that showcase adaptability, collaboration, and clear communication.

2.5 Stage 5: Final/Onsite Round

The final round generally consists of a series of interviews with data team leads, analytics managers, and potential cross-functional partners. You may be asked to present a data project, walk through your approach to a complex analytics problem, or respond to follow-up questions on previous technical or behavioral rounds. This stage assesses both your technical depth and your ability to present findings, justify analytical choices, and adapt your communication style to different stakeholders. Preparation should include rehearsing presentations and being ready to discuss both the business impact and technical details of your past work.

2.6 Stage 6: Offer & Negotiation

If you are successful through all prior stages, you will receive an offer from the recruiter or HR representative. This conversation will cover compensation, benefits, start date, and any other final questions. Be prepared to discuss your expectations and clarify any aspects of the role or package.

2.7 Average Timeline

The typical IHS Global Data Analyst interview process spans 3–5 weeks from application to offer. Candidates with highly relevant experience and prompt scheduling may move through the process in as little as 2–3 weeks, while standard timelines involve about a week between each stage. The technical and final rounds may be condensed into a single onsite day or split over multiple sessions, depending on interviewer availability and candidate preference.

Next, let’s dive into the types of questions you can expect during each stage of the IHS Global Data Analyst interview process.

3.1 Data Analysis & Business Impact

This topic focuses on your ability to analyze data, draw actionable insights, and connect those insights to business outcomes. Expect questions about designing analyses, evaluating experiments, and making recommendations that align with organizational goals.

3.1.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Highlight your approach to simplifying technical findings, using visualization, and tailoring your message to different stakeholders. Emphasize adaptability and ensuring insights lead to informed decisions.

3.1.2 Describing a data project and its challenges
Describe the context, the specific hurdles faced, and how you navigated technical or organizational obstacles. Focus on problem-solving skills and the impact of your solutions.

3.1.3 Making data-driven insights actionable for those without technical expertise
Discuss methods for translating complex findings into practical recommendations, using clear language and relatable examples. Emphasize collaboration and cross-functional communication.

3.1.4 You work as a data scientist for ride-sharing company. An executive asks how you would evaluate whether a 50% rider discount promotion is a good or bad idea? How would you implement it? What metrics would you track?
Outline how you’d design the experiment, define success metrics (like retention or profit), and analyze results with statistical rigor. Consider both short-term and long-term business impact.

3.1.5 store-performance-analysis
Explain the metrics and dimensions you’d use to assess performance, such as sales, conversion rates, and customer demographics. Show how you’d uncover drivers of performance and present actionable recommendations.

3.2 Data Engineering & Pipeline Design

These questions assess your understanding of building, maintaining, and optimizing data pipelines and data warehouses. You’ll be expected to demonstrate knowledge of scalable data architecture and robust ETL practices.

3.2.1 Design a data warehouse for a new online retailer
Describe your approach to schema design, data integration, and supporting both transactional and analytical queries. Emphasize scalability and the ability to adapt to future business needs.

3.2.2 Design a robust, scalable pipeline for uploading, parsing, storing, and reporting on customer CSV data.
Outline the stages of data ingestion, validation, and storage, and discuss how you’d ensure reliability, error handling, and reporting.

3.2.3 Design a data pipeline for hourly user analytics.
Explain how you’d process and aggregate large volumes of data efficiently, including considerations for latency, fault tolerance, and future scaling.

3.2.4 Let's say that you're in charge of getting payment data into your internal data warehouse.
Discuss ETL strategies, data validation, and how you’d ensure data integrity and security, especially with sensitive financial information.

3.2.5 How would you design a data warehouse for a e-commerce company looking to expand internationally?
Focus on handling multi-region data, localization, and supporting analytics across diverse business units and geographies.

3.3 SQL & Data Manipulation

This category evaluates your proficiency in SQL and your ability to perform advanced data manipulation. Expect questions on aggregations, pivots, and efficient querying for business reporting.

3.3.1 Write a query to compute the average time it takes for each user to respond to the previous system message
Detail your use of window functions, joins, and time difference calculations to align and analyze user interactions.

3.3.2 Write a query to create a pivot table that shows total sales for each branch by year
Explain how you’d use conditional aggregation or pivot functions to summarize and present multi-dimensional sales data.

3.3.3 Calculate total and average expenses for each department.
Describe grouping, aggregation, and formatting to produce clear, actionable financial summaries.

3.3.4 Categorize sales based on the amount of sales and the region
Discuss using case statements or conditional logic to segment data and support targeted analysis.

3.4 Experimentation & Statistical Analysis

These questions explore your ability to design, execute, and interpret experiments such as A/B tests, and to ensure statistical validity in your conclusions.

3.4.1 An A/B test is being conducted to determine which version of a payment processing page leads to higher conversion rates. You’re responsible for analyzing the results. How would you set up and analyze this A/B test? Additionally, how would you use bootstrap sampling to calculate the confidence intervals for the test results, ensuring your conclusions are statistically valid?
Describe test design, metric selection, and use of statistical techniques to analyze results and quantify uncertainty.

3.4.2 The role of A/B testing in measuring the success rate of an analytics experiment
Explain when and how to use A/B testing, the importance of control groups, and how you’d interpret and communicate results.

3.4.3 How would you approach improving the quality of airline data?
Discuss data profiling, identifying and correcting errors, and establishing ongoing quality controls.

3.4.4 You’re tasked with analyzing data from multiple sources, such as payment transactions, user behavior, and fraud detection logs. How would you approach solving a data analytics problem involving these diverse datasets? What steps would you take to clean, combine, and extract meaningful insights that could improve the system's performance?
Outline your approach to data cleaning, joining disparate sources, and generating insights that drive business improvements.

3.5 Behavioral Questions

3.5.1 Tell me about a time you used data to make a decision.
Focus on a specific example where your analysis led to a measurable business outcome, detailing your process and the impact.

3.5.2 Describe a challenging data project and how you handled it.
Highlight the complexity, your approach to overcoming obstacles, and the results you achieved.

3.5.3 How do you handle unclear requirements or ambiguity?
Explain your strategies for clarifying objectives, communicating with stakeholders, and iterating toward a solution.

3.5.4 Tell me about a time when your colleagues didn’t agree with your approach. What did you do to bring them into the conversation and address their concerns?
Showcase your collaboration and communication skills, describing how you built consensus and moved the project forward.

3.5.5 Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?
Describe how you adapted your communication style and ensured your message was understood.

3.5.6 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?
Discuss your methods for prioritization, setting boundaries, and maintaining project focus.

3.5.7 When leadership demanded a quicker deadline than you felt was realistic, what steps did you take to reset expectations while still showing progress?
Explain how you managed expectations, communicated trade-offs, and delivered incremental results.

3.5.8 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Highlight your ability to persuade and build alignment using evidence and clear reasoning.

3.5.9 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.
Demonstrate how you leveraged visual tools and iterative feedback to drive consensus.

3.5.10 Tell us about a time you caught an error in your analysis after sharing results. What did you do next?
Show your accountability, how you communicated the mistake, and the steps you took to correct it and prevent future issues.

4.1 Company-specific tips:

Familiarize yourself with IHS Global’s core industries—energy, finance, transportation, and technology. Understanding the types of data and analytics challenges unique to these sectors will help you tailor your interview responses and showcase your domain awareness.

Research how IHS Global leverages data analytics to deliver market intelligence and support strategic decision-making for clients. Prepare to discuss how your analytical skills can contribute to actionable insights in fast-moving, data-rich environments.

Review recent news, reports, or press releases about IHS Global’s major initiatives, acquisitions, or product launches. This knowledge will help you frame your answers in the context of the company’s current business landscape and priorities.

Be ready to discuss your approach to presenting findings to both technical and non-technical audiences, as IHS Global values analysts who can bridge the gap between complex data and practical business recommendations.

4.2 Role-specific tips:

4.2.1 Demonstrate expertise in data cleaning and organization.
Be prepared to walk through your process for handling messy, incomplete, or inconsistent data. Share specific examples of how you identified and resolved data quality issues, and explain the impact of your work on business outcomes or project success.

4.2.2 Practice designing scalable data pipelines and warehouses.
Expect technical questions about building robust ETL processes, integrating disparate data sources, and optimizing pipelines for reliability and scalability. Articulate your approach to schema design, data validation, and automation, especially for large and complex datasets.

4.2.3 Showcase your ability to communicate insights to diverse stakeholders.
Prepare stories that highlight how you tailored your presentations for executives, product managers, or external clients. Focus on clarity, adaptability, and the use of visualizations or prototypes to make data-driven recommendations accessible and actionable.

4.2.4 Strengthen your SQL and data manipulation skills.
Anticipate exercises involving aggregations, pivots, window functions, and conditional logic. Explain your reasoning as you write queries, emphasizing efficiency and accuracy in extracting meaningful insights from large tables.

4.2.5 Demonstrate proficiency in statistical analysis and experimentation.
Be ready to discuss how you design and analyze A/B tests, calculate confidence intervals, and interpret experiment results. Use examples to show how you’ve measured the impact of business initiatives and ensured statistical validity in your conclusions.

4.2.6 Prepare examples of analyzing data from multiple sources.
Share your approach to joining, cleaning, and synthesizing diverse datasets—such as payment transactions, user behavior logs, and operational metrics. Highlight your ability to extract actionable insights that drive system improvements or support strategic decisions.

4.2.7 Highlight your problem-solving and adaptability in ambiguous situations.
Describe times when you faced unclear requirements or shifting project scopes. Emphasize your strategies for clarifying objectives, communicating with stakeholders, and delivering solutions despite ambiguity.

4.2.8 Illustrate your collaboration and stakeholder management skills.
Prepare examples of negotiating scope, resolving disagreements, and influencing without formal authority. Showcase your ability to build consensus and keep projects on track through effective communication and prioritization.

4.2.9 Show accountability and attention to detail in your work.
Be ready to discuss how you handle errors in your analysis, including how you communicate mistakes and implement safeguards to prevent recurrence. This demonstrates your commitment to data integrity and continuous improvement.

4.2.10 Practice presenting complex projects with business impact.
Rehearse how you would walk interviewers through a challenging data project, focusing on your analytical approach, the obstacles you overcame, and the tangible outcomes for your team or organization. Tailor your narrative to highlight both technical depth and business relevance.

5.1 How hard is the IHS Global Data Analyst interview?
The IHS Global Data Analyst interview is rigorous but fair, focusing on both technical depth and business acumen. You’ll be challenged on core skills like data cleaning, pipeline design, SQL proficiency, and statistical analysis, as well as your ability to translate complex findings into actionable business insights. Candidates who prepare thoroughly—especially with examples from cross-functional projects—will find the process engaging and rewarding.

5.2 How many interview rounds does IHS Global have for Data Analyst?
Typically, the process includes 4–6 rounds: an initial resume screen, recruiter call, one or more technical/case interviews, a behavioral interview, and a final onsite or virtual round with team leads or cross-functional stakeholders. Each stage is designed to assess both technical expertise and communication skills.

5.3 Does IHS Global ask for take-home assignments for Data Analyst?
While not always required, some candidates may be given take-home assignments or case studies. These usually involve analyzing a dataset, designing a pipeline, or preparing a brief report on business insights. The goal is to evaluate your practical problem-solving and your ability to present findings clearly.

5.4 What skills are required for the IHS Global Data Analyst?
Key skills include advanced SQL, data cleaning and organization, data pipeline and warehouse design, statistical analysis (including A/B testing), and the ability to communicate insights to both technical and non-technical audiences. Familiarity with business intelligence tools and experience working with large, diverse datasets are highly valued.

5.5 How long does the IHS Global Data Analyst hiring process take?
The typical timeline is 3–5 weeks from initial application to offer, with some candidates progressing faster depending on scheduling and availability. Each stage generally takes about a week, and the technical and final rounds may be combined for efficiency.

5.6 What types of questions are asked in the IHS Global Data Analyst interview?
Expect a mix of technical questions on SQL, data pipelines, and statistical analysis, as well as business case scenarios and behavioral questions. You’ll be asked to solve real-world data problems, discuss your approach to ambiguous requirements, and demonstrate how you communicate insights to stakeholders with varying levels of technical expertise.

5.7 Does IHS Global give feedback after the Data Analyst interview?
IHS Global usually provides feedback through recruiters, especially if you progress to later stages. While technical feedback may be brief, you’ll often receive insights into strengths and areas for development. Don’t hesitate to ask for specific feedback to help guide future preparation.

5.8 What is the acceptance rate for IHS Global Data Analyst applicants?
The acceptance rate is competitive, estimated around 3–7% for qualified candidates. Strong technical skills, clear communication, and relevant industry experience can help you stand out.

5.9 Does IHS Global hire remote Data Analyst positions?
Yes, IHS Global offers remote and hybrid options for Data Analyst roles, depending on the team and project needs. Some positions may require occasional office visits for collaboration, but remote work is increasingly supported across the organization.

• Related guides:
  • IHS Global interview questions
  • Data Analyst interview guide
  • Top Data Analyst interview tips

Ready to ace your IHS Global Data Analyst interview? It’s not just about knowing the technical skills—you need to think like an IHS Global Data Analyst, 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 Global and similar companies.

With resources like the IHS Global Data Analyst 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 cleaning and organization, pipeline design, stakeholder communication, and statistical analysis—each mapped to the skills IHS Global values most.

Take the next step—explore more case study questions, try mock interviews, and browse targeted prep materials on Interview Query. Bookmark this guide or share it with peers prepping for similar roles. It could be the difference between applying and offering. You’ve got this!