Tigloo Data Engineer Interview Guide

Getting ready for a Data Engineer interview at Tigloo? The Tigloo Data Engineer interview process typically spans 4–6 question topics and evaluates skills in areas like SQL, data pipeline design, ETL processes, and communicating data insights to both technical and non-technical audiences. Interview preparation is especially important for this role at Tigloo, as candidates are expected to navigate complex data environments, leverage modern cloud-based tools like Azure Data Factory and Power BI, and contribute to technology renewal projects that drive operational efficiency for global clients.

In preparing for the interview, you should:

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

Tigloo is a Spanish IT services and consulting company specializing in digital transformation, cloud solutions, and managed IT services for businesses across various sectors. The company partners with clients to optimize their technological infrastructure, streamline data management, and enhance operational efficiency. As a Data Engineer at Tigloo, you will contribute to projects focused on modernizing data extraction, transformation, and analysis processes, particularly for industrial clients, leveraging Microsoft Fabric technologies such as Azure Data Factory and Power BI. Tigloo values technical expertise, collaboration, and client-centered service to drive digital innovation.

As a Junior Data Engineer at Tigloo, you will support the IT department of a leading industrial client in Navarra by participating in technology renewal projects focused on data extraction, transformation, and analysis. Your core responsibilities include developing and maintaining data pipelines using tools like SQL Server, Azure Data Factory, and ETL solutions, as well as collaborating with team members to ensure efficient data management. Experience with Microsoft Fabric technologies and Power BI (DAX) is highly valued. You will contribute to optimizing data processes and enabling data-driven decision-making, while fostering positive relationships within the team and with end users in a hybrid work environment based in Pamplona.

2.1 Stage 1: Application & Resume Review

During the initial application phase, Tigloo’s recruitment team conducts a thorough review of your CV and cover letter, focusing on your technical experience with SQL Server, ETL tools, and data pipeline development. Familiarity with Microsoft Fabric technologies such as Azure Data Factory and Power BI (DAX) is highly valued, as is any prior work in IT departments supporting industrial clients. Emphasize projects that demonstrate hands-on skills in data extraction, transformation, and analysis, and highlight collaborative experience within hybrid work environments.

2.2 Stage 2: Recruiter Screen

The recruiter screen is typically a 20-30 minute phone or video interview with Tigloo’s HR representative. Expect a discussion of your motivation for joining Tigloo, your communication skills, and your ability to work in hybrid teams. The recruiter will also confirm practical requirements such as your location (proximity to Pamplona), language proficiency, and availability. Prepare by clearly articulating why you are interested in data engineering at Tigloo, and how your interpersonal skills contribute to a positive work environment.

2.3 Stage 3: Technical/Case/Skills Round

This round is conducted by senior data engineers or IT managers and is focused on assessing your technical depth and problem-solving abilities. You may be asked to design or troubleshoot data pipelines, optimize SQL queries, or discuss your approach to ETL process failures. Expect practical scenarios involving Microsoft Fabric (especially Azure Data Factory and Power BI), and be ready to explain your choices between Python and SQL for specific tasks. You might also face system design questions (e.g., building a scalable data warehouse or transforming batch ingestion to real-time streaming), as well as exercises in data cleaning and aggregation. Preparation should include reviewing the fundamentals of data pipeline architecture, SQL optimization, and ETL troubleshooting.

2.4 Stage 4: Behavioral Interview

The behavioral interview is conducted by the hiring manager or a cross-functional panel, and explores your teamwork, adaptability, and client-facing skills. You’ll be asked to describe how you’ve handled challenges in previous data projects, how you communicate complex insights to non-technical stakeholders, and how you contribute to a collaborative team environment. Prepare examples that demonstrate empathy, problem-solving under pressure, and your ability to make data accessible to diverse audiences.

2.5 Stage 5: Final/Onsite Round

The final round is often an onsite or extended virtual session with the IT department and potential end-users. This stage may include a combination of technical case studies, system design tasks, and presentations of data insights tailored for different audiences. You may also participate in group exercises or meet with department leaders to discuss your approach to technology renewal and long-term data strategy. Demonstrate your ability to work in hybrid settings, your understanding of business needs, and your readiness to integrate into Tigloo’s client-driven culture.

2.6 Stage 6: Offer & Negotiation

Once you successfully complete the interview rounds, Tigloo’s HR team will reach out to discuss compensation, benefits, and onboarding logistics. This is your opportunity to clarify expectations regarding remote work, training on Microsoft Fabric tools, and career development within the company.

2.7 Average Timeline

The standard Tigloo Data Engineer interview process spans approximately 3 to 5 weeks from initial application to offer, with each stage typically separated by a few days to a week. Fast-track candidates with strong technical backgrounds and relevant Microsoft Fabric experience may move through the process in as little as 2-3 weeks, while the average pace allows time for scheduling and panel coordination, especially for hybrid onsite sessions.

Next, let’s dive into the types of interview questions you can expect throughout the Tigloo Data Engineer process.

3.1 Data Pipeline Design & ETL

Data pipeline design and ETL are central to the data engineer role at Tigloo. Expect questions on architecting robust, scalable solutions for ingesting, transforming, and aggregating diverse data sources, along with troubleshooting pipeline failures and ensuring data integrity.

3.1.1 Design a data pipeline for hourly user analytics
Outline the end-to-end architecture, including data ingestion, transformation, aggregation, and storage. Specify technologies, error handling, and scalability considerations.
Example answer: "I’d use a streaming platform like Kafka for ingestion, Spark for transformation and aggregation, and store results in a partitioned data warehouse. Monitoring and retries would ensure reliability."

3.1.2 Let's say that you're in charge of getting payment data into your internal data warehouse
Describe the ETL process, schema design, and validation steps to ensure accurate, timely ingestion. Highlight how you handle sensitive data and maintain data quality.
Example answer: "I’d build a scheduled ETL job to extract, validate, and load payment data with encryption and audit logs, handling schema changes via versioning."

3.1.3 Redesign batch ingestion to real-time streaming for financial transactions
Compare batch and streaming architectures, discuss trade-offs, and propose a solution using technologies like Kafka, Spark Streaming, or Flink.
Example answer: "Migrating to Kafka and Spark Streaming would allow near real-time data availability, with checkpoints and replay for fault tolerance."

3.1.4 Design a robust, scalable pipeline for uploading, parsing, storing, and reporting on customer CSV data
Explain how you’d automate validation, error handling, and schema inference for large CSV files, emphasizing modularity and scalability.
Example answer: "I’d use a cloud function to trigger parsing, validate schema, and store in a managed warehouse. Automated error reports would flag issues for review."

3.1.5 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners
Discuss how you handle schema diversity, data mapping, and transformation logic across multiple sources, ensuring consistency and reliability.
Example answer: "I’d implement a schema registry, use mapping templates per partner, and build transformation jobs with automated data quality checks."

3.2 Data Warehousing & System Architecture

Data engineers at Tigloo are expected to design and optimize data warehouses and large-scale systems. Be prepared to discuss schema design, storage solutions, and system reliability for analytics and reporting.

3.2.1 Design a data warehouse for a new online retailer
Describe key tables, partitioning, indexing strategies, and how to support analytics queries efficiently.
Example answer: "I’d organize data around fact tables for transactions and dimension tables for products and customers, using partitioning by date and indexing on customer IDs."

3.2.2 System design for a digital classroom service
Lay out the architecture for managing user data, course content, and analytics, focusing on scalability, security, and real-time data needs.
Example answer: "I’d separate user, course, and activity data into microservices, use event-driven updates, and secure PII with role-based access controls."

3.2.3 Design a solution to store and query raw data from Kafka on a daily basis
Explain how you’d persist streaming data, partition it for efficient querying, and manage schema evolution.
Example answer: "I’d sink Kafka streams into a time-partitioned data lake, use schema-on-read for flexibility, and optimize queries with metadata indexing."

3.2.4 Design a reporting pipeline for a major tech company using only open-source tools under strict budget constraints
List open-source technologies for ETL, storage, visualization, and monitoring, and describe how you’d ensure reliability and cost-effectiveness.
Example answer: "I’d use Airflow for orchestration, PostgreSQL for storage, and Metabase for reporting, with containerized deployment for scalability."

3.3 Data Quality & Troubleshooting

Maintaining high data quality and diagnosing pipeline issues are crucial for data engineers at Tigloo. Expect questions on systematic approaches to data validation, error handling, and resolving transformation failures.

3.3.1 Ensuring data quality within a complex ETL setup
Describe your strategy for validating incoming data, monitoring pipeline health, and remediating discrepancies.
Example answer: "I’d implement automated data profiling, set up anomaly alerts, and use reconciliation scripts to compare source and destination tables."

3.3.2 How would you systematically diagnose and resolve repeated failures in a nightly data transformation pipeline?
Explain your troubleshooting workflow, including logging, dependency checks, and rollback procedures.
Example answer: "I’d review error logs, isolate failing components, test with controlled inputs, and implement retry logic with alerting for persistent issues."

3.3.3 Describing a real-world data cleaning and organization project
Share your approach to profiling, cleaning, and documenting messy datasets, and how you ensured reproducibility.
Example answer: "I’d start with exploratory profiling, apply rule-based cleaning scripts, and maintain versioned cleaning notebooks for auditability."

3.3.4 Challenges of specific student test score layouts, recommended formatting changes for enhanced analysis, and common issues found in "messy" datasets
Discuss how you’d restructure data for analysis, automate cleaning, and handle inconsistencies.
Example answer: "I’d normalize formats, standardize missing values, and use automated scripts to detect and resolve layout issues."

3.4 SQL & Data Manipulation

Strong SQL skills are essential for data engineers at Tigloo. Expect questions testing your ability to write efficient queries, aggregate data, and handle complex filtering requirements.

3.4.1 Write a SQL query to count transactions filtered by several criterias
Demonstrate filtering, aggregation, and optimization techniques for large transaction datasets.
Example answer: "I’d use WHERE clauses for each filter, GROUP BY relevant columns, and ensure indexes support the query for performance."

3.4.2 Modifying a billion rows
Explain strategies for bulk updates, minimizing downtime, and preserving data integrity in massive tables.
Example answer: "I’d batch updates, use partitioning, and leverage database features like bulk operations and transaction logs."

3.4.3 Write code to generate a sample from a multinomial distribution with keys
Describe your approach to probabilistic sampling and how you’d implement it efficiently.
Example answer: "I’d use a weighted random selection algorithm, ensuring reproducibility and scalability for large datasets."

3.5 Communication & Stakeholder Management

Data engineers at Tigloo must communicate technical concepts clearly and collaborate with non-technical stakeholders. Expect questions on presenting insights, making data accessible, and adapting communication to different audiences.

3.5.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Discuss how you tailor visualizations and explanations for business versus technical audiences.
Example answer: "I’d use simple visuals, focus on actionable metrics, and adapt language to match stakeholder expertise."

3.5.2 Demystifying data for non-technical users through visualization and clear communication
Share techniques for simplifying technical content and fostering stakeholder understanding.
Example answer: "I’d use analogies, interactive dashboards, and step-by-step walkthroughs to bridge the gap."

3.5.3 Making data-driven insights actionable for those without technical expertise
Describe how you translate complex findings into concrete recommendations for decision-makers.
Example answer: "I’d summarize key trends, highlight business impact, and propose clear next steps."

3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision.
Describe the business context, your analysis process, and the impact of your recommendation.
Example answer: "I analyzed user engagement data to identify drop-off points and recommended a UI change that increased retention by 15%."

3.6.2 Describe a challenging data project and how you handled it.
Outline the obstacles, your problem-solving approach, and the project outcome.
Example answer: "During a migration, I encountered schema mismatches and resolved them by automating mapping scripts and collaborating with the engineering team."

3.6.3 How do you handle unclear requirements or ambiguity?
Explain your strategy for clarifying goals, iterating with stakeholders, and documenting assumptions.
Example answer: "I schedule stakeholder interviews, define success metrics, and maintain a change log to track evolving requirements."

3.6.4 Walk us through how you handled conflicting KPI definitions between two teams and arrived at a single source of truth.
Describe your approach to aligning stakeholders, standardizing definitions, and implementing governance.
Example answer: "I facilitated workshops, documented consensus definitions, and updated dashboards to reflect unified KPIs."

3.6.5 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Share how you built credibility, presented evidence, and navigated resistance.
Example answer: "I presented pilot results, highlighted business benefits, and gained buy-in through targeted workshops."

3.6.6 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?
Explain your prioritization framework, communication strategy, and outcome.
Example answer: "I quantified the impact of new requests, presented trade-offs, and secured leadership sign-off to protect project timelines."

3.6.7 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 how you assessed missingness, chose treatment methods, and communicated uncertainty.
Example answer: "I profiled missing data, used statistical imputation, and shaded unreliable sections in reports to maintain transparency."

3.6.8 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Describe the automation tools, monitoring setup, and impact on team efficiency.
Example answer: "I built scheduled validation scripts and alerting dashboards, reducing manual review time by 80%."

3.6.9 Tell me about a time you exceeded expectations during a project.
Share how you identified opportunities for improvement and delivered measurable results.
Example answer: "I automated a manual reporting process, saving 10 hours per week and enabling faster decision-making."

3.6.10 Describe a situation where two source systems reported different values for the same metric. How did you decide which one to trust?
Explain your reconciliation process, validation steps, and how you communicated findings.
Example answer: "I traced data lineage, compared source documentation, and performed cross-system audits before recommending the trusted source."

4.1 Company-specific tips:

Deepen your understanding of Tigloo’s core business in digital transformation, cloud solutions, and managed IT services. Research how Tigloo partners with clients—especially in industrial sectors—to modernize data infrastructure and drive operational efficiency. Familiarize yourself with the company’s emphasis on client-centered service, collaboration, and technology renewal projects, as these themes often surface in both technical and behavioral interviews.

Demonstrate your familiarity with Microsoft Fabric technologies, particularly Azure Data Factory and Power BI. Tigloo highly values candidates who can effectively leverage these tools to build, automate, and optimize data pipelines and reporting solutions. Review how these platforms are used in real-world scenarios to extract, transform, and analyze data for business decision-making.

Prepare to discuss your experience working in hybrid or multi-location teams. Tigloo supports a collaborative, hybrid work environment, so be ready to share examples of how you communicate across distributed teams and maintain productivity and positive relationships with both technical colleagues and non-technical stakeholders.

Showcase your adaptability and client-facing skills. Tigloo’s projects often involve direct collaboration with end users and require translating complex technical concepts into actionable business insights. Practice explaining technical solutions in clear, accessible language and be ready to describe how you have handled shifting requirements or ambiguous project goals in the past.

4.2 Role-specific tips:

Master the fundamentals of data pipeline architecture, with a focus on end-to-end design for extraction, transformation, and loading (ETL) processes. Be prepared to walk through the architecture of robust, scalable pipelines—detailing your approach to data ingestion, transformation logic, error handling, and storage. Use examples from your experience or hypothetical scenarios relevant to Tigloo’s industrial clients.

Strengthen your skills in SQL Server, as well as your ability to write efficient, optimized queries for large datasets. Practice crafting queries that aggregate, filter, and transform data, and be ready to explain your strategies for handling complex joins, bulk updates, and performance tuning in high-volume environments.

Showcase your expertise with Azure Data Factory. Review how to orchestrate data workflows, set up triggers, handle data movement between on-premises and cloud sources, and monitor pipeline health. Be ready to troubleshoot common ETL issues and discuss how you ensure data reliability and quality within Azure-based solutions.

Demonstrate your knowledge of Power BI, especially in building dashboards and reports that make data accessible to non-technical users. Highlight your experience with DAX for data modeling and calculations, and discuss how you tailor visualizations to support actionable business insights for different stakeholders.

Prepare to discuss your approach to data quality and troubleshooting. Be able to articulate systematic methods for validating incoming data, monitoring pipeline health, and resolving transformation failures. Share real examples of how you have automated data-quality checks or remediated data inconsistencies in previous projects.

Practice communicating technical concepts to non-technical audiences. Tigloo values engineers who can bridge the gap between IT and business, so refine your ability to present complex data solutions in a way that is clear, concise, and relevant to end users. Use analogies, simple visuals, and focus on business impact in your explanations.

Anticipate behavioral questions about teamwork, adaptability, and stakeholder management. Prepare stories that showcase your problem-solving skills, your ability to work under pressure, and your commitment to delivering client value. Emphasize how you contribute to a positive team culture and handle challenges with empathy and professionalism.

5.1 How hard is the Tigloo Data Engineer interview?
The Tigloo Data Engineer interview is moderately challenging, especially for candidates new to cloud-based data engineering. The process emphasizes hands-on technical skills in SQL, ETL pipeline architecture, and experience with Microsoft Fabric technologies like Azure Data Factory and Power BI. Expect scenario-driven questions that test your ability to design, troubleshoot, and communicate data solutions in real-world industrial contexts. Candidates who prepare thoroughly and understand Tigloo’s focus on technology renewal and client service have a strong chance of success.

5.2 How many interview rounds does Tigloo have for Data Engineer?
Typically, there are 4–6 interview rounds. These include an initial application and resume review, a recruiter screen, one or two technical rounds focused on data engineering skills, a behavioral interview exploring teamwork and stakeholder management, and a final onsite or virtual round with the IT department and client representatives. The process is designed to assess both technical depth and cultural fit.

5.3 Does Tigloo ask for take-home assignments for Data Engineer?
While Tigloo’s process primarily centers on live technical interviews and case studies, some candidates may be given a short take-home exercise. This usually involves designing a data pipeline or troubleshooting an ETL scenario using SQL or Azure Data Factory. The assignment is practical and reflects real challenges faced in Tigloo’s client projects.

5.4 What skills are required for the Tigloo Data Engineer?
Key skills include advanced SQL, ETL pipeline design, experience with Azure Data Factory, Power BI (especially DAX), and data warehousing concepts. Proficiency in troubleshooting data quality issues, optimizing data workflows, and communicating technical solutions to both technical and non-technical stakeholders is crucial. Familiarity with Microsoft Fabric technologies and experience working in hybrid or client-facing environments are highly valued.

5.5 How long does the Tigloo Data Engineer hiring process take?
The typical timeline is 3–5 weeks from initial application to offer. Fast-track candidates with strong technical backgrounds may complete the process in as little as 2–3 weeks, while the average pace allows for scheduling multiple panel interviews and hybrid onsite sessions. Timelines can vary based on candidate availability and client project schedules.

5.6 What types of questions are asked in the Tigloo Data Engineer interview?
Expect a mix of technical and behavioral questions. Technical questions cover data pipeline design, ETL troubleshooting, SQL optimization, and use of Azure Data Factory and Power BI. You’ll also face scenario-based questions about data quality, system architecture, and stakeholder communication. Behavioral questions focus on teamwork, adaptability, and delivering value in client-driven projects.

5.7 Does Tigloo give feedback after the Data Engineer interview?
Tigloo typically provides feedback through their HR team after each interview stage. The feedback is usually high-level, highlighting strengths and areas for improvement. Detailed technical feedback may be limited, but candidates are encouraged to ask for clarification to guide future preparation.

5.8 What is the acceptance rate for Tigloo Data Engineer applicants?
While Tigloo does not publish specific acceptance rates, the Data Engineer role is competitive, especially for candidates with Microsoft Fabric and Azure Data Factory experience. Industry estimates suggest an acceptance rate of around 5–8% for qualified applicants, reflecting the technical and collaborative demands of the position.

5.9 Does Tigloo hire remote Data Engineer positions?
Yes, Tigloo supports hybrid work arrangements for Data Engineers, with a mix of remote and onsite collaboration in Pamplona. Some roles may require occasional office visits or onsite client meetings, but the company is flexible and values the ability to work effectively in distributed teams. Be sure to clarify expectations regarding remote work during the offer and negotiation stage.

• Related guides:
  • Tigloo interview questions
  • Data Engineer interview guide
  • “Top Data Engineering interview tips”

Ready to ace your Tigloo Data Engineer interview? It’s not just about knowing the technical skills—you need to think like a Tigloo 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 Tigloo and similar companies.

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