Decskill Data Engineer Interview Guide

Getting ready for a Data Engineer interview at Decskill? The Decskill Data Engineer interview process typically spans a variety of technical and scenario-based question topics, evaluating skills in areas like data pipeline design, large-scale data processing, ETL workflow optimization, and communicating complex insights to diverse stakeholders. Interview preparation is essential for this role at Decskill, as candidates are expected to demonstrate not only expertise in building robust data infrastructure, but also the ability to collaborate across teams and adapt solutions to real-world business needs.

In preparing for the interview, you should:

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

Decskill is an IT consulting company founded in 2014, dedicated to delivering value through knowledge, talent, and technology. With over 600 professionals and offices in Lisbon, Porto, Madrid, and Luxembourg, Decskill operates across three main areas: Talent (IT team extension and digital transformation), Boost (software development and time-to-market optimization), and Connect (IT infrastructure consulting and management). The company emphasizes a culture of excellence, diversity, and inclusion, investing in the growth and well-being of its people. As a Data Engineer, you will play a key role in designing and optimizing data solutions that support digital transformation and innovation for Decskill’s clients.

As a Data Engineer at Decskill, you will be responsible for designing, developing, and maintaining scalable data pipelines and platforms that support business objectives and digital transformation initiatives. You will work with large and complex datasets, leveraging technologies such as SQL, Python, Power BI, and Big Data tools to ensure efficient data processing, integration, and visualization. Collaborating closely with cross-functional teams—including data analysts, product managers, and business stakeholders—you will help define use cases, optimize data workflows, and communicate technical information to non-technical audiences. Your role contributes directly to delivering high-quality, reliable data solutions that drive innovation and add value for Decskill’s clients.

2.1 Stage 1: Application & Resume Review

The interview journey at Decskill for Data Engineer roles begins with a detailed application and resume screening. At this stage, recruiters and technical leads review your CV to assess your experience in data engineering, proficiency in SQL and Python, hands-on exposure to data pipeline development, and familiarity with cloud platforms (such as Azure or AWS). They also look for evidence of experience with data visualization tools, ETL processes, and your ability to communicate technical concepts to non-technical stakeholders. To prepare, ensure your resume clearly highlights relevant projects, technologies, and quantifiable achievements that align with Decskill’s focus on robust data solutions and cross-functional collaboration.

2.2 Stage 2: Recruiter Screen

If your profile matches Decskill’s requirements, you’ll be invited to a recruiter screen, typically a 20-30 minute phone or video call. The recruiter will discuss your background, motivation for joining Decskill, and clarify aspects of your experience, such as your involvement in data architecture, pipeline automation, or data quality initiatives. Expect questions about your availability, willingness to work in a hybrid or on-site model in Lisbon or Porto, and your English proficiency. To prepare, review your career narrative, be ready to explain your technical and business communication skills, and demonstrate enthusiasm for Decskill’s mission of digital transformation.

2.3 Stage 3: Technical/Case/Skills Round

The technical evaluation is a core part of the Decskill process and usually involves one or more rounds with senior data engineers or technical managers. This stage may include live coding exercises, system design scenarios, or in-depth case studies focused on data pipeline design, ETL troubleshooting, and scalable data processing. You may be asked to walk through real-world data cleaning projects, discuss the challenges of processing large datasets, or design a data warehouse or reporting pipeline. Emphasis is placed on your ability to handle data quality issues, optimize performance, and select appropriate tools (e.g., SQL, Python, Spark, Airflow, Power BI). Preparation should focus on hands-on practice, reviewing end-to-end pipeline architecture, and being ready to justify your technical choices in context.

2.4 Stage 4: Behavioral Interview

Behavioral interviews at Decskill explore how you approach teamwork, problem-solving, and communication in a consulting environment. Interviewers—often hiring managers or future colleagues—will probe for examples of cross-functional collaboration, managing competing priorities, and adapting technical messaging for non-technical audiences. Expect to discuss experiences where you drove innovation, overcame hurdles in data projects, or made data insights accessible to business stakeholders. Prepare by reflecting on your past projects, focusing on your impact, adaptability, and alignment with Decskill’s values of growth, inclusion, and excellence.

2.5 Stage 5: Final/Onsite Round

The final stage typically involves a series of in-depth interviews with technical leads, project managers, and sometimes client representatives. This round may combine technical deep-dives (such as system design for a digital classroom or payment data pipeline), business case discussions, and further behavioral assessment. You may also be asked to present a complex data project, articulate insights, or solve a whiteboard problem under time constraints. The goal here is to evaluate your holistic fit: technical mastery, communication, and your ability to contribute to Decskill’s client-driven, innovation-focused culture. Prepare by reviewing your portfolio, practicing clear and structured explanations, and demonstrating both technical breadth and business acumen.

2.6 Stage 6: Offer & Negotiation

Once you successfully complete all interview rounds, the HR or recruiting team will reach out with an offer. This stage includes discussions about compensation, benefits, work location (hybrid or on-site), and start date. Decskill values transparency and alignment, so be prepared to discuss your expectations and clarify any logistical questions. Having a clear understanding of your priorities and the market landscape will help you navigate this phase confidently.

2.7 Average Timeline

The typical Decskill Data Engineer interview process spans 3-5 weeks from initial application to final offer. Fast-track candidates with highly relevant experience or internal referrals may complete the process in as little as two weeks, while standard timelines usually involve a week between each major stage. Scheduling flexibility, especially for technical and onsite rounds, can influence the overall duration. Throughout the process, maintaining clear communication with your recruiter can help you stay informed and prepared for next steps.

Next, let’s dive into the specific interview questions you’re likely to encounter at Decskill for Data Engineer roles.

3.1 Data Pipeline Design & ETL

Expect questions on designing, optimizing, and troubleshooting data pipelines for large-scale, real-world scenarios. Focus on demonstrating your understanding of scalable architecture, handling heterogeneous data sources, and ensuring data integrity throughout the ETL process.

3.1.1 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners
Discuss how you would architect a modular pipeline, select appropriate tools for ingestion, transformation, and loading, and address schema variability. Emphasize monitoring, error handling, and scalability.

Example answer: "I would build a modular ETL pipeline using Apache Airflow for orchestration and Spark for scalable processing. To handle heterogeneous schemas, I’d implement schema mapping logic and validate incoming data. Monitoring and alerting would be set up for ingestion errors, ensuring data consistency and reliability."

3.1.2 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes
Explain how you would architect a pipeline from raw ingestion to serving predictions, including data cleaning, feature engineering, and model deployment. Highlight considerations for real-time versus batch processing.

Example answer: "I’d use a streaming platform like Kafka to ingest rental data, process it in near real-time with Spark Streaming, and store cleaned features in a data warehouse. For predictions, a REST API would serve model outputs, with scheduled batch jobs retraining the model as new data arrives."

3.1.3 Design a robust, scalable pipeline for uploading, parsing, storing, and reporting on customer CSV data
Describe the steps to ensure reliability and scalability in ingesting large CSV files, including validation, error handling, and reporting. Discuss automation and monitoring strategies.

Example answer: "I’d automate CSV ingestion using cloud storage triggers and serverless compute for parsing. Data validation and error logging would be built in, with parsed results stored in a relational database. Reporting dashboards would be updated via scheduled jobs, ensuring scalability and reliability."

3.1.4 Design a data pipeline for hourly user analytics
Outline the pipeline architecture for aggregating user activity data hourly, including storage, processing, and visualization components. Address data latency and fault tolerance.

Example answer: "I’d stream user events into a cloud data lake, aggregate hourly metrics using Spark jobs, and store results in a queryable warehouse. Visualization would be handled by BI tools, with robust error handling to ensure data is complete and timely."

3.1.5 Design a reporting pipeline for a major tech company using only open-source tools under strict budget constraints
Identify cost-effective open-source solutions for data ingestion, transformation, storage, and reporting. Discuss trade-offs and how you would ensure performance and maintainability.

Example answer: "I’d use Apache NiFi for ingestion, Spark for transformation, and PostgreSQL for storage. Reporting would be handled by Metabase or Superset. I’d ensure maintainability through containerization and automate deployments using CI/CD pipelines."

3.2 Data Modeling & Warehousing

These questions test your ability to design scalable, flexible data models and warehouses that support complex analytics. Focus on normalization, schema design, and optimizing for query performance.

3.2.1 Design a data warehouse for a new online retailer
Describe your approach to modeling transactional, customer, and product data, and optimizing for analytics queries. Discuss schema choices and indexing strategies.

Example answer: "I’d use a star schema with fact tables for transactions and dimension tables for customers and products. Partitioning and indexing would optimize query performance, and ETL jobs would ensure data freshness."

3.2.2 System design for a digital classroom service
Explain how you would model entities such as students, courses, assignments, and interactions for analytics and reporting. Discuss scalability and future extensibility.

Example answer: "I’d design normalized tables for students, courses, and assignments, with junction tables for enrollments and submissions. Indexing key columns would support fast queries, and the schema would be extensible for new features."

3.2.3 Let's say that you're in charge of getting payment data into your internal data warehouse
Describe the ingestion, transformation, and loading process for payment data, including handling edge cases and ensuring data accuracy.

Example answer: "I’d set up automated ingestion from payment APIs, transform data to standardize formats, and load it into a warehouse. Data validation would catch inconsistencies, and periodic audits would ensure accuracy."

3.3 Data Quality & Cleaning

You will be asked about strategies for identifying, diagnosing, and resolving data quality issues in large, messy datasets. Demonstrate your approach to profiling, cleaning, and validating data for robust analytics.

3.3.1 Describing a real-world data cleaning and organization project
Walk through your process for profiling, cleaning, and validating a messy dataset, including tools and techniques used.

Example answer: "I start by profiling data for missing values and inconsistencies, then use Python and SQL for cleaning. Validation checks and reproducible scripts ensure transparency and allow stakeholders to audit my work."

3.3.2 How would you approach improving the quality of airline data?
Describe your approach to identifying and resolving data quality issues, such as missing or inconsistent records, and setting up automated quality checks.

Example answer: "I’d profile the dataset for common issues, implement automated validation scripts, and set up dashboards to monitor data quality over time. Root-cause analysis would guide long-term fixes."

3.3.3 Challenges of specific student test score layouts, recommended formatting changes for enhanced analysis, and common issues found in "messy" datasets
Explain how you would reformat and clean complex data layouts to enable reliable analysis, including handling outliers and missing values.

Example answer: "I’d restructure the layout for consistency, apply transformations to handle outliers and missing values, and document all changes for reproducibility."

3.3.4 Write a function that splits the data into two lists, one for training and one for testing
Describe how you would implement data splitting with core Python, ensuring randomization and reproducibility.

Example answer: "I’d shuffle the data and slice it into training and testing sets using Python’s built-in functions, documenting the random seed for reproducibility."

3.3.5 Ensuring data quality within a complex ETL setup
Discuss methods for monitoring and maintaining data quality across multiple ETL pipelines, including logging and alerting.

Example answer: "I’d implement logging at each ETL stage, set up automated alerts for anomalies, and schedule regular audits to ensure ongoing data quality."

3.4 Coding & Algorithms

Expect practical coding challenges that test your ability to manipulate data efficiently, optimize queries, and solve common algorithmic problems encountered in engineering data solutions.

3.4.1 Given a string, write a function to find its first recurring character
Describe your approach to solving this problem efficiently, considering time and space complexity.

Example answer: "I’d iterate through the string, storing seen characters in a set. On encountering a duplicate, I’d return that character immediately, ensuring O(n) time complexity."

3.4.2 Write a function to return the names and ids for ids that we haven't scraped yet
Explain how you would compare two lists or datasets to identify missing elements and return relevant information.

Example answer: "I’d use set operations to compare the scraped and unscripted IDs, then filter and return the corresponding names and IDs efficiently."

3.4.3 Write a function that splits the data into two lists, one for training and one for testing
Discuss how you would implement a randomized split using only basic Python tools.

Example answer: "I’d shuffle the data with random.sample and slice it into training and test sets, ensuring the proportions match requirements."

3.4.4 Write a function to find the append frequency of a given value in a list
Explain how you would count occurrences efficiently and handle edge cases.

Example answer: "I’d use a dictionary to track frequencies as I iterate through the list, returning the count for the target value."

3.5 Data Analysis & Experimentation

These questions assess your ability to design, implement, and interpret analytics experiments. Focus on A/B testing, metric selection, and communicating results to technical and non-technical stakeholders.

3.5.1 The role of A/B testing in measuring the success rate of an analytics experiment
Discuss how you would design an A/B test, select metrics, and interpret results to measure experiment success.

Example answer: "I’d randomly assign users to control and treatment groups, define clear success metrics, and use statistical tests to compare outcomes, ensuring proper sample size and validity."

3.5.2 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?
Describe your approach to evaluating promotions using data, including experiment design and key metrics.

Example answer: "I’d run a controlled experiment, track metrics like conversion rate, retention, and profitability, and analyze the impact on both short-term usage and long-term customer value."

3.5.3 What kind of analysis would you conduct to recommend changes to the UI?
Explain how you would analyze user journey data to identify pain points and recommend actionable UI improvements.

Example answer: "I’d analyze user flow data, segment by cohort, and identify drop-off points. Recommendations would be based on conversion analysis and user feedback."

3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision.
Focus on a project where your analysis directly impacted business outcomes. Highlight the decision, your process, and the measurable result.

3.6.2 Describe a challenging data project and how you handled it.
Choose a project with technical or stakeholder hurdles. Emphasize your problem-solving, adaptability, and the final resolution.

3.6.3 How do you handle unclear requirements or ambiguity?
Share your strategy for clarifying goals—proactive communication, iterative prototyping, and frequent stakeholder check-ins.

3.6.4 Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?
Explain your approach to simplifying complex concepts, using visualizations, and tailoring your message to the audience.

3.6.5 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 prioritization frameworks, quantifying effort, and effective communication to maintain project integrity.

3.6.6 You’re given a dataset that’s full of duplicates, null values, and inconsistent formatting. The deadline is soon, but leadership wants insights from this data for tomorrow’s decision-making meeting. What do you do?
Describe your triage approach—profiling, prioritizing fixes, and communicating the limitations and reliability bands of your results.

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

3.6.8 How do you prioritize multiple deadlines? Additionally, how do you stay organized when you have multiple deadlines?
Explain your use of task management systems, time-boxing, and communication to balance competing priorities.

3.6.9 Tell me about a project where you had to make a tradeoff between speed and accuracy.
Discuss the context, your decision process, and how you communicated risks and outcomes to stakeholders.

3.6.10 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.
Describe how early visualization or prototyping helped converge requirements and accelerate consensus.

4.1 Company-specific tips:

Become familiar with Decskill’s consulting-driven approach and their commitment to digital transformation for clients. Understand how data engineering supports these initiatives across sectors like Talent, Boost, and Connect, and be prepared to discuss how robust data pipelines and infrastructure enable innovation and operational excellence.

Research Decskill’s core values—excellence, diversity, and inclusion—and think about how your work style and past experiences align with their culture. Be ready to share stories that reflect your adaptability, commitment to learning, and ability to thrive in diverse teams.

Learn about Decskill’s geographical presence and be prepared to discuss your flexibility regarding hybrid or on-site work in Lisbon, Porto, or other locations. Highlight your openness to collaborating with distributed teams and supporting clients across different regions.

Review recent Decskill projects or case studies if available, and prepare to reference examples of how data engineering can drive measurable business impact for consulting clients. This demonstrates your understanding of the company’s mission and your ability to deliver value in a client-focused environment.

4.2 Role-specific tips:

4.2.1 Master end-to-end data pipeline design, including ingestion, transformation, and reporting.
Practice articulating how you would architect scalable ETL workflows that handle heterogeneous data sources, automate error handling, and deliver clean, reliable data for analytics and reporting. Be ready to discuss specific tools (e.g., Airflow, Spark, SQL, Python) and justify your choices based on scenario requirements.

4.2.2 Be prepared to troubleshoot and optimize large-scale data processing workflows.
Demonstrate your ability to identify bottlenecks, optimize performance, and ensure fault tolerance in complex pipelines. Practice explaining how you monitor, profile, and refactor jobs to handle increasing data volumes or changing business needs.

4.2.3 Showcase your experience with data quality and cleaning in real-world scenarios.
Prepare examples of projects where you profiled, cleaned, and validated messy datasets under tight deadlines. Discuss your approach to automating data-quality checks, handling missing values, and making data usable for decision-making.

4.2.4 Highlight your skills in data modeling and warehouse design.
Review best practices for designing flexible, scalable schemas that support analytics and reporting. Be ready to discuss normalization, indexing, and how you optimize for query performance and future extensibility.

4.2.5 Demonstrate your ability to communicate complex technical concepts to non-technical stakeholders.
Practice explaining pipeline architecture, data quality issues, and analytics results in clear, business-focused language. Share examples of how you’ve adapted your messaging for different audiences and used visualizations (e.g., Power BI) to drive understanding and alignment.

4.2.6 Prepare for practical coding and algorithm challenges using SQL and Python.
Sharpen your ability to write efficient functions for common data engineering tasks—such as splitting datasets, finding recurring values, or comparing lists—using core programming concepts. Focus on clarity, performance, and handling edge cases.

4.2.7 Reflect on your experience collaborating across teams and managing project ambiguity.
Think of stories where you clarified requirements, negotiated scope, and aligned stakeholders with different priorities. Emphasize your proactive communication and your ability to keep projects on track in dynamic environments.

4.2.8 Be ready to discuss tradeoffs in speed, accuracy, and scalability.
Prepare to talk about situations where you balanced rapid delivery with data integrity, and how you communicated risks and limitations to leadership or clients.

4.2.9 Practice sharing examples of driving innovation and process improvement.
Highlight times when you automated repetitive tasks, improved data reliability, or introduced new tools and workflows that made a measurable difference for your team or clients.

4.2.10 Prepare to discuss experimentation and analytics, including A/B testing and metric selection.
Be ready to describe how you design and interpret analytics experiments, select meaningful metrics, and communicate actionable insights to both technical and business stakeholders.

5.1 How hard is the Decskill Data Engineer interview?
The Decskill Data Engineer interview is challenging, with a strong focus on practical experience designing and optimizing data pipelines, large-scale ETL workflows, and handling real-world data quality issues. Candidates are expected to demonstrate technical depth in SQL, Python, and cloud platforms, as well as the ability to communicate complex concepts to diverse stakeholders. The process rewards those who can blend technical excellence with consulting acumen and adaptability to dynamic project requirements.

5.2 How many interview rounds does Decskill have for Data Engineer?
Typically, the Decskill Data Engineer interview includes five main rounds: application & resume review, recruiter screen, technical/case/skills round, behavioral interview, and a final onsite or virtual round. Each stage is designed to assess both your technical expertise and your fit with Decskill’s client-driven, collaborative culture.

5.3 Does Decskill ask for take-home assignments for Data Engineer?
Take-home assignments are occasionally used, especially for candidates who need to demonstrate hands-on pipeline design, data cleaning, or analytics skills. These assignments usually involve building or troubleshooting a small ETL workflow, profiling a messy dataset, or presenting insights based on real business scenarios. The goal is to evaluate your practical approach and ability to deliver reliable, scalable solutions under deadlines.

5.4 What skills are required for the Decskill Data Engineer?
Key skills for Decskill Data Engineers include advanced SQL and Python programming, designing and optimizing ETL pipelines, experience with cloud platforms (Azure, AWS), data modeling and warehousing, data quality assurance, and proficiency with BI tools like Power BI. Strong communication, stakeholder management, and the ability to translate technical solutions into business impact are also essential.

5.5 How long does the Decskill Data Engineer hiring process take?
The typical timeline for the Decskill Data Engineer hiring process is 3-5 weeks from initial application to final offer. Fast-track candidates may complete the process in as little as two weeks, while scheduling logistics and technical rounds can extend the duration. Clear communication with recruiters helps keep the process on track.

5.6 What types of questions are asked in the Decskill Data Engineer interview?
Expect a mix of technical and scenario-based questions covering data pipeline design, ETL troubleshooting, data modeling, data cleaning, and coding challenges in SQL and Python. Behavioral questions explore your experience collaborating across teams, managing ambiguous requirements, and communicating with non-technical stakeholders. You may also be asked to present previous projects or solve case studies relevant to Decskill’s consulting context.

5.7 Does Decskill give feedback after the Data Engineer interview?
Decskill typically provides high-level feedback through recruiters, especially regarding fit and technical strengths. Detailed technical feedback may be limited, but you can expect clarity on next steps and insights into areas for improvement if you advance through multiple rounds.

5.8 What is the acceptance rate for Decskill Data Engineer applicants?
While specific acceptance rates are not published, the role is competitive given Decskill’s focus on consulting excellence and technical depth. The estimated acceptance rate is around 5-8% for qualified applicants who demonstrate strong data engineering and cross-functional skills.

5.9 Does Decskill hire remote Data Engineer positions?
Yes, Decskill offers remote Data Engineer positions, with flexibility for hybrid or on-site work in locations such as Lisbon and Porto. Some roles may require occasional office visits or travel for client engagement, but the company supports distributed teams and cross-regional collaboration.

• Related guides:
  • Decskill interview questions
  • Data Engineer interview guide
  • Top data engineering interview tips

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

With resources like the Decskill 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.

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!