Globant Business Intelligence Interview Guide

Getting ready for a Business Intelligence interview at Globant? The Globant Business Intelligence interview process typically spans 3–5 question topics and evaluates skills in areas like presenting actionable insights, SQL data analysis, data modeling and visualization, and business problem-solving. Interview preparation is particularly important for this role at Globant, as candidates are expected to communicate complex findings effectively, design scalable data solutions, and tailor their recommendations to a variety of stakeholders in fast-moving, client-driven environments.

In preparing for the interview, you should:

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

Globant is a global IT and software development company specializing in digital transformation, consulting, and technology services for organizations across diverse industries. With a focus on innovation, Globant delivers end-to-end solutions in areas such as artificial intelligence, cloud computing, and business intelligence, helping clients adapt to rapidly evolving digital landscapes. Operating in over 25 countries, the company emphasizes a culture of creativity, collaboration, and continuous learning. As a Business Intelligence professional at Globant, you will contribute to data-driven decision-making and support clients in leveraging insights to achieve strategic goals.

As a Business Intelligence professional at Globant, you are responsible for transforming raw data into actionable insights that inform strategic business decisions. You will gather, analyze, and visualize data from various sources, creating dashboards and reports to support internal teams and client projects. Collaboration with stakeholders such as product managers, engineers, and business leaders is essential to define data requirements and identify key performance indicators. Your work enables Globant and its clients to optimize operations, track progress toward goals, and uncover new opportunities, directly contributing to the company’s commitment to digital innovation and data-driven solutions.

2.1 Stage 1: Application & Resume Review

The process begins with an in-depth review of your resume and application materials by the Globant recruitment team. They look for demonstrated experience with business intelligence tools, strong SQL skills, data modeling, analytics, and the ability to create and present actionable insights. Experience with data visualization platforms, such as Power BI or Tableau, and a track record of translating business requirements into technical solutions are highly valued. Ensuring your resume highlights relevant BI projects, technical proficiencies, and impact-driven outcomes will help you stand out at this stage.

2.2 Stage 2: Recruiter Screen

Next, a recruiter will reach out for a 30–45 minute phone or video call. This conversation covers your background, motivations for joining Globant, and a high-level discussion of your technical and business intelligence experience. The recruiter may touch on your familiarity with SQL, data visualization, and your ability to communicate complex insights to non-technical stakeholders. Preparation should include a concise summary of your BI journey, key achievements, and alignment with Globant’s culture and values.

2.3 Stage 3: Technical/Case/Skills Round

Candidates who pass the recruiter screen are invited to one or more technical interviews. These typically include a practical assessment, such as a take-home case study or live technical test, where you’ll be asked to process, model, and visualize a dataset, often using SQL and BI tools. You may also be required to present your findings, demonstrating your ability to communicate insights clearly and adapt your presentation to different audiences. Additional technical questions often focus on database design, ETL processes, and analytics problem-solving. Some interviews may be conducted in English to assess communication skills in a global context. To prepare, review your experience with end-to-end BI solutions, practice articulating your thought process, and be ready to justify your analytical choices.

2.4 Stage 4: Behavioral Interview

A behavioral interview is usually conducted by a hiring manager or senior team member, focusing on your previous experiences, collaboration style, and how you handle challenges in BI projects. Expect to discuss your approach to overcoming data quality issues, managing stakeholder expectations, and driving business impact through analytics. Questions may also explore your adaptability, growth mindset, and ability to work in cross-functional, multicultural teams. Reflect on specific examples where you demonstrated leadership, problem-solving, and effective communication.

2.5 Stage 5: Final/Onsite Round

The final stage often involves multiple interviews with project leads from both Globant and its clients. These interviews may delve deeper into technical skills, business acumen, and your fit for specific client projects. You may be asked to solve advanced SQL problems, design BI solutions for hypothetical business scenarios, or explain your approach to data governance and visualization. Cultural fit and the ability to represent Globant in client-facing situations are also assessed. In some cases, you may be interviewed by the client directly, especially if the role is embedded within a client team.

2.6 Stage 6: Offer & Negotiation

Candidates who successfully complete all previous rounds receive an offer from Globant’s HR team. This stage involves discussing compensation, benefits, start date, and, if applicable, project or client alignment. There may be some flexibility in negotiation, especially regarding role seniority or project placement, depending on your experience and performance throughout the process.

2.7 Average Timeline

The typical Globant Business Intelligence interview process spans 3–6 weeks from initial application to final offer. Some candidates may experience a faster process if project needs are urgent or their profiles are a strong match, potentially completing all rounds within 2–3 weeks. However, scheduling interviews with both internal teams and clients can introduce variability, and some candidates report longer timelines, especially if multiple client interviews are required or if there are delays in feedback. Prompt communication and flexibility in scheduling can help expedite your journey through the process.

Next, let’s explore the types of questions you can expect at each stage of the Globant Business Intelligence interview process.

3.1 SQL & Data Querying

Expect questions that assess your ability to extract, manipulate, and aggregate data using SQL. Globant values efficiency, clarity, and scalability in query logic, especially for business-critical reporting. Be ready to discuss your approach to handling large datasets, complex filters, and performance optimization.

3.1.1 Write a SQL query to count transactions filtered by several criterias.
Focus on constructing queries that apply multiple filters and aggregate results efficiently. Discuss indexing and query optimization for large transaction tables.

3.1.2 Write a query to compute the average time it takes for each user to respond to the previous system message
Explain how to use window functions to align related messages, calculate time differences, and group by user for average response times.

3.1.3 Write a query to calculate the conversion rate for each trial experiment variant
Aggregate trial data by variant, count conversions, and divide by total users per group. Clarify your method for handling missing or incomplete conversion data.

3.1.4 How would you design a solution to store and query raw data from Kafka on a daily basis?
Describe schema design, partitioning strategies, and efficient querying for high-volume clickstream data.

3.1.5 Describe a real-world data cleaning and organization project
Walk through your approach to profiling, cleaning, and documenting messy datasets, emphasizing reproducibility and auditability.

3.2 Data Warehousing & ETL

These questions gauge your experience with designing, maintaining, and scaling data warehouses and ETL pipelines. Globant looks for candidates who can architect robust solutions for diverse business domains, ensuring data quality and accessibility.

3.2.1 How would you design a data warehouse for a e-commerce company looking to expand internationally?
Discuss schema design, localization challenges, and strategies for integrating multiple sources and currencies.

3.2.2 Design a data warehouse for a new online retailer
Outline your approach to fact and dimension tables, scalability, and supporting business analytics.

3.2.3 Ensuring data quality within a complex ETL setup
Explain monitoring, validation, and reconciliation techniques for multi-source ETL environments.

3.2.4 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes.
Describe ingestion, cleaning, feature engineering, and serving layers in your pipeline design.

3.3 Experimentation & Metrics

These questions evaluate your ability to design experiments, measure success, and interpret business metrics. Globant emphasizes actionable insights and statistical rigor in business intelligence work.

3.3.1 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?
Discuss experiment setup, metrics, statistical tests, and bootstrapping for confidence intervals.

3.3.2 The role of A/B testing in measuring the success rate of an analytics experiment
Explain how you design experiments, select KPIs, and interpret results for business decisions.

3.3.3 Building a model to predict if a driver on Uber will accept a ride request or not
Describe your approach to feature selection, modeling, and evaluation metrics in predictive analytics.

3.3.4 Let’s say that you work at TikTok. The goal for the company next quarter is to increase the daily active users metric (DAU).
Discuss strategies for analyzing DAU, identifying drivers, and recommending interventions.

3.3.5 How would you evaluate whether a 50% rider discount promotion is a good or bad idea? How would you implement it? What metrics would you track?
Describe experiment design, metric selection, and analysis for promotion effectiveness.

3.4 Data Presentation & Insight Communication

Globant expects BI professionals to translate complex data into clear, actionable insights for diverse audiences. These questions test your ability to visualize, explain, and adapt findings for stakeholders.

3.4.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Discuss tailoring presentations, using data storytelling, and adapting technical detail to audience needs.

3.4.2 Making data-driven insights actionable for those without technical expertise
Explain your approach to demystifying analytics, using analogies and clear visuals.

3.4.3 Demystifying data for non-technical users through visualization and clear communication
Describe techniques for building intuitive dashboards and communicating uncertainty.

3.4.4 How would you visualize data with long tail text to effectively convey its characteristics and help extract actionable insights?
Explain your visualization choices and how you surface actionable patterns in sparse data.

3.5 Behavioral Questions

3.5.1 Tell me about a time you used data to make a decision.
Describe how your analysis led to a concrete business outcome, emphasizing the recommendation and impact.

3.5.2 How do you handle unclear requirements or ambiguity?
Share how you clarify objectives, iterate with stakeholders, and document assumptions to ensure value delivery.

3.5.3 Describe a challenging data project and how you handled it.
Walk through the obstacles, your problem-solving approach, and how you ensured successful completion.

3.5.4 Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?
Explain your strategy for bridging technical gaps and fostering understanding with non-technical audiences.

3.5.5 Describe a situation where two source systems reported different values for the same metric. How did you decide which one to trust?
Discuss your validation process, reconciliation steps, and how you communicated findings.

3.5.6 How do you prioritize multiple deadlines? Additionally, how do you stay organized when you have multiple deadlines?
Share your prioritization framework, time management tools, and communication with stakeholders.

3.5.7 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Describe the tools and processes you implemented, and the resulting improvement in data reliability.

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

3.5.9 Tell me about a time you delivered critical insights even though 30% of the dataset had nulls. What analytical trade-offs did you make?
Discuss your approach to handling missing data, communicating limitations, and ensuring actionable recommendations.

3.5.10 Explain how you communicated uncertainty to executives when your cleaned dataset covered only 60% of total transactions.
Describe your transparency strategy, use of confidence intervals, and how you maintained stakeholder trust.

4.1 Company-specific tips:

Get familiar with Globant’s client-driven business model and its emphasis on digital transformation across industries. Research how Globant leverages data and analytics to drive innovative solutions for clients in sectors like retail, finance, and entertainment. This will enable you to tailor your answers to real business scenarios and demonstrate an understanding of Globant’s strategic priorities.

Understand Globant’s culture of collaboration and continuous learning. Prepare to discuss how you work effectively in multicultural, cross-functional teams, and how you contribute to a creative, agile environment. Highlight any experience you have with consulting or working directly with clients, as Globant values professionals who can represent the company in client-facing roles.

Review recent Globant projects, case studies, or press releases that showcase their use of AI, cloud computing, and business intelligence. Be ready to reference these examples in your interview to show that you’re up-to-date with the company’s latest initiatives and can connect your BI skills to their evolving technology landscape.

4.2 Role-specific tips:

4.2.1 Practice communicating complex insights for diverse audiences.
Focus on your ability to present actionable insights to both technical and non-technical stakeholders. Prepare examples where you tailored your communication style, used storytelling, or visualizations to make data-driven recommendations clear and compelling. Globant values candidates who can bridge the gap between data and decision-makers, so practice explaining technical findings in simple terms.

4.2.2 Demonstrate advanced SQL, data modeling, and visualization skills.
Refine your SQL proficiency by tackling problems involving multi-step filtering, time-based aggregations, and window functions. Be ready to discuss your approach to designing scalable data models and choosing appropriate visualization techniques for different business needs. Prepare sample stories of building dashboards or reports that drove strategic decisions.

4.2.3 Show how you solve real business problems with data.
Prepare to walk through end-to-end BI solutions you’ve designed, from gathering requirements and cleaning data to modeling, analysis, and presenting actionable recommendations. Use examples that highlight your impact—such as improving conversion rates, optimizing processes, or enabling new business opportunities through analytics.

4.2.4 Highlight your experience with data warehousing and ETL pipelines.
Review your approach to architecting data warehouses for scalability and flexibility, especially in scenarios involving multiple data sources or internationalization. Be ready to discuss how you ensure data quality, handle reconciliation issues, and automate validation checks in complex ETL environments.

4.2.5 Prepare for experimentation and metrics analysis.
Brush up on your knowledge of A/B testing, statistical significance, and metrics selection. Be ready to design experiments, analyze results, and use techniques like bootstrap sampling to communicate confidence intervals. Share examples of how your analysis led to actionable business changes.

4.2.6 Practice handling ambiguous requirements and messy data.
Think through how you clarify objectives with stakeholders, iterate on deliverables, and document assumptions when requirements are unclear. Prepare stories where you dealt with incomplete or inconsistent datasets, describing your approach to data cleaning, analytical trade-offs, and transparent communication about limitations.

4.2.7 Showcase your stakeholder management and alignment skills.
Prepare to discuss how you use wireframes, prototypes, or iterative feedback to align stakeholders with different visions. Highlight your ability to foster consensus and ensure that BI deliverables meet both business and technical needs.

4.2.8 Illustrate your ability to automate and improve BI processes.
Share examples of how you automated data-quality checks, reporting workflows, or dashboard updates. Emphasize the impact of these improvements—such as increased reliability, reduced manual effort, or faster time-to-insight.

4.2.9 Be ready to discuss data governance and communication of uncertainty.
Prepare to explain your approach to handling missing or incomplete data, communicating uncertainty, and maintaining trust with stakeholders. Share examples of how you use confidence intervals, document limitations, and ensure that recommendations remain actionable even when data coverage is imperfect.

4.2.10 Prepare for behavioral questions focused on impact, adaptability, and growth mindset.
Reflect on experiences where you overcame challenges, drove business impact, or contributed to a culture of learning. Be ready to discuss how you prioritize deadlines, stay organized, and continuously develop your BI skills to meet evolving business needs.

5.1 How hard is the Globant Business Intelligence interview?
The Globant Business Intelligence interview is challenging and comprehensive, focusing on both technical depth and business acumen. You’ll be assessed on advanced SQL, data modeling, data visualization, and your ability to present actionable insights to diverse audiences. Expect real-world scenarios, case studies, and behavioral questions that test your problem-solving skills, adaptability, and stakeholder management. Candidates who thrive in client-driven, fast-paced environments and can communicate complex findings clearly will stand out.

5.2 How many interview rounds does Globant have for Business Intelligence?
Typically, the Globant Business Intelligence interview process consists of 4–6 rounds. These include an initial recruiter screen, one or more technical/case interviews (often featuring a take-home assignment or live technical test), a behavioral interview, and a final round with project leads or clients. The exact number may vary depending on the specific client project and team requirements.

5.3 Does Globant ask for take-home assignments for Business Intelligence?
Yes, many candidates report receiving a take-home case study or technical assessment. These assignments often require you to analyze a dataset, design a BI solution, and present your findings, demonstrating your analytical skills, technical proficiency, and ability to communicate insights effectively.

5.4 What skills are required for the Globant Business Intelligence?
Key skills include advanced SQL, data modeling, experience with BI tools like Power BI or Tableau, data warehousing, ETL pipeline design, and statistical analysis. Strong business problem-solving abilities, stakeholder management, and the capacity to present insights in a clear, actionable manner are essential. Experience with data governance, handling messy or incomplete data, and automating BI processes will further strengthen your profile.

5.5 How long does the Globant Business Intelligence hiring process take?
The process usually takes 3–6 weeks from application to offer. Timelines can be shorter (2–3 weeks) if there’s an urgent project need or your profile closely matches the requirements. Scheduling interviews with both Globant and client teams can add variability, so prompt communication and flexibility help expedite the process.

5.6 What types of questions are asked in the Globant Business Intelligence interview?
You’ll encounter technical questions on SQL, data modeling, visualization, and data warehousing. Expect case studies on business problem-solving, experimentation, and metrics analysis. Behavioral questions will probe your collaboration style, adaptability, and stakeholder management. You may also be asked to present data insights, handle ambiguous requirements, and discuss how you automate or improve BI processes.

5.7 Does Globant give feedback after the Business Intelligence interview?
Globant typically provides feedback through recruiters, especially after final rounds. While detailed technical feedback may be limited, you can expect high-level insights about your performance, strengths, and any gaps identified during the process.

5.8 What is the acceptance rate for Globant Business Intelligence applicants?
Exact rates aren’t published, but the process is competitive due to the technical and client-facing nature of the role. An estimated 5–8% of applicants progress to offer, with higher chances for those who demonstrate strong BI skills, business impact, and adaptability in multicultural environments.

5.9 Does Globant hire remote Business Intelligence positions?
Yes, Globant offers remote opportunities for Business Intelligence roles, especially as many client projects and teams operate globally. Some positions may require occasional travel or onsite meetings for collaboration, depending on client needs and project requirements.

• Related guides:
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  • Business Intelligence interview guide
  • Top Business Intelligence interview tips
  • Top 12 Business Intelligence Case Studies (Updated in 2025)

Ready to ace your Globant Business Intelligence interview? It’s not just about knowing the technical skills—you need to think like a Globant Business Intelligence professional, 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 Globant and similar companies.

With resources like the Globant Business Intelligence 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.

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