Centro Data Engineer Interview Guide

Getting ready for a Data Engineer interview at Centro? The Centro Data Engineer interview process typically spans multiple question topics and evaluates skills in areas like data pipeline design, SQL and Python programming, cloud data platforms, and scalable data architecture. Interview preparation is especially important for this role at Centro, where candidates are expected to demonstrate technical depth in building robust data solutions and the ability to communicate complex concepts clearly to both technical and business stakeholders. As Centro continues its journey to become a truly data-driven company, Data Engineers play a pivotal role in shaping innovative data flows and enabling informed decision-making across the organization.

In preparing for the interview, you should:

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

Centro is an independent technology company specializing in open banking solutions for the banking and finance sector. Originating from the Sella Group’s innovation initiatives, Centro develops fast, modular, and cost-effective information systems to drive digital transformation in financial services. The company is committed to becoming a data-driven organization, leveraging advanced data management and analytics to optimize internal processes and support group-wide business decisions. As a Data Engineer, you will play a pivotal role in designing and maintaining data pipelines, enabling innovative use of data to enhance operational efficiency and inform strategic decision-making.

As a Data Engineer at Centro, you will play a key role in the Data Management area, designing and developing data pipelines to acquire, process, and distribute information within the company’s data platform. You will ensure the effective availability of complex data sets to support business decision-making and transparency, create data models for various use cases, and help define data access controls. Collaborating closely with multidisciplinary teams such as Business, IT, Compliance, and Risk, you will support Group companies in optimizing processes through innovative data usage. This position is crucial in Centro’s journey to become a data-driven company, driving technological improvements and best practices in open banking and finance.

2.1 Stage 1: Application & Resume Review

Your application and resume are reviewed by the data management and engineering leadership team, with a strong emphasis on experience in designing and building data pipelines, advanced SQL and Python proficiency, and hands-on cloud platform expertise (Microsoft, Oracle). Proven success in managing large-scale data flows, OLAP/OLTP systems, and data governance is highly valued. Highlighting collaborative work with multidisciplinary teams and evidence of problem-solving in complex data environments will help you stand out.

2.2 Stage 2: Recruiter Screen

A recruiter or HR representative will reach out for a brief screening, typically by phone or video call. This conversation covers your background, motivation for joining Centro, and high-level alignment with the company’s data-driven mission and open banking focus. Expect questions about your previous data engineering roles, salary expectations, and your approach to working with both technical and business stakeholders. Preparation should include clear articulation of your career trajectory and readiness to contribute to innovation in financial data platforms.

2.3 Stage 3: Technical/Case/Skills Round

You’ll be invited to a technical assessment, either as a take-home assignment or a live interview. This stage centers on SQL coding, Python scripting, and potentially Java, with tasks involving data pipeline design, ETL processes, and system architecture for scalable data solutions. Expect challenges such as writing queries for complex aggregations, optimizing data flows, and demonstrating your understanding of data warehousing concepts. Preparation should focus on real-world problem solving, efficient code structuring, and clear documentation of your solutions.

2.4 Stage 4: Behavioral Interview

The behavioral interview typically involves the VP of Engineering and/or senior data team members. You’ll discuss your experience managing data projects, overcoming hurdles in data quality and pipeline reliability, and collaborating across business, IT, and compliance teams. Expect to demonstrate your communication skills, adaptability, and ability to translate technical insights for non-technical audiences. Preparation should include examples of cross-functional teamwork, stakeholder engagement, and driving process improvements through innovative data usage.

2.5 Stage 5: Final/Onsite Round

The onsite round consists of multiple interviews (usually 3–5 sessions, 45 minutes each) with various team members from engineering, analytics, and business functions. These interviews delve into advanced technical topics—such as system design for data platforms, optimization of data models, and troubleshooting ETL failures—alongside whiteboard problem solving and presentation of data-driven insights. You may be asked to present a case study or walk through a technical project, emphasizing clarity, scalability, and business impact. Preparation should include rehearsing presentations, reviewing architecture best practices, and readiness for in-depth technical and interpersonal evaluation.

2.6 Stage 6: Offer & Negotiation

Once you’ve completed all rounds, the recruiter will reach out regarding compensation, benefits, and contract details. At Centro, salary expectations are discussed transparently, and the offer package may include flexible working policies, health coverage, and opportunities for professional development. Prepare to negotiate confidently, backed by market research and a clear understanding of your value to the team.

2.7 Average Timeline

The Centro Data Engineer interview process typically spans 3–5 weeks from initial application to final offer. Fast-track candidates with strong technical alignment and relevant experience may complete the process in 2–3 weeks, while standard pace involves about a week between each stage. Take-home technical assessments usually have a 2–3 day completion window, and onsite rounds are scheduled based on team availability. Communication may vary depending on the volume of applicants, so proactive follow-up is recommended.

Next, let’s explore the specific interview questions commonly asked throughout the Centro Data Engineer process.

3.1 Data Pipeline Design & ETL

Data engineering at Centro is heavily focused on designing robust, scalable, and reliable data pipelines. You’ll need to demonstrate your ability to architect ETL processes, handle large-scale data ingestion, and ensure data quality across diverse sources.

3.1.1 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners.
Outline your approach to handling diverse data formats, scheduling, error handling, and monitoring. Discuss how you would ensure schema evolution and data quality as new partners are added.

3.1.2 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes.
Describe how you would architect the pipeline, including data ingestion, transformation, storage, and serving layers. Highlight how you’d optimize for batch and real-time use cases.

3.1.3 Design a robust, scalable pipeline for uploading, parsing, storing, and reporting on customer CSV data.
Explain how you’d handle schema validation, error detection, and reporting. Emphasize automation and scalability, especially for high-volume uploads.

3.1.4 How would you systematically diagnose and resolve repeated failures in a nightly data transformation pipeline?
Discuss root cause analysis, logging strategies, alerting, and remediation. Show your process for minimizing downtime and documenting solutions.

3.1.5 Design a solution to store and query raw data from Kafka on a daily basis.
Describe your approach for ingesting, partitioning, and storing streaming data, as well as querying for analytics. Mention considerations for scalability and fault tolerance.

3.2 SQL & Data Modeling

Centro’s data engineering interviews frequently assess your SQL expertise and ability to design data models that efficiently support analytics and reporting. Expect to write queries and discuss schema design for real-world scenarios.

3.2.1 Write a SQL query to compute the median household income for each city.
Describe how you’d use window functions or aggregate logic to calculate a median per group. Address edge cases like missing data or ties.

3.2.2 Write a query to select the top 3 departments with at least ten employees and rank them according to the percentage of their employees making over 100K in salary.
Explain your use of aggregation, filtering, and ranking functions. Discuss strategies for optimizing query performance on large datasets.

3.2.3 Select the 2nd highest salary in the engineering department.
Demonstrate your approach using subqueries or ranking functions. Be clear about handling duplicates and nulls.

3.2.4 Calculate total and average expenses for each department.
Provide a solution using GROUP BY and aggregate functions, and discuss how you’d handle departments with missing or zero expenses.

3.2.5 Given a list of locations that your trucks are stored at, return the top location for each model of truck (Mercedes or BMW).
Show how you’d use grouping and ranking to identify the most frequent location per model. Address how you’d handle ties and missing data.

3.3 Data Warehousing & System Design

Centro values engineers who can design scalable data architectures and warehouses to support evolving business needs. Be ready to discuss trade-offs, schema choices, and system reliability.

3.3.1 Design a data warehouse for a new online retailer.
Describe your approach to schema design, partitioning, and supporting analytics. Discuss considerations for scalability and future expansion.

3.3.2 How would you design a data warehouse for a e-commerce company looking to expand internationally?
Explain handling multi-region data, localization, and compliance. Highlight strategies for optimizing query performance across large datasets.

3.3.3 Model a database for an airline company.
Discuss your approach to relational modeling, normalization, and supporting operational and analytical queries. Address handling historical and real-time data.

3.3.4 System design for a digital classroom service.
Outline key components, data flows, and storage solutions. Focus on scalability, user management, and data privacy.

3.3.5 Design the system supporting an application for a parking system.
Describe your approach to data modeling, real-time updates, and integration with external systems. Address reliability and performance considerations.

3.4 Data Quality & Cleaning

Ensuring data integrity is critical for Centro’s data engineers. You’ll be asked about real-world data cleaning, profiling, and quality assurance in high-volume environments.

3.4.1 Describing a real-world data cleaning and organization project.
Share your process for profiling, cleaning, and validating data. Emphasize reproducibility and communication of limitations to stakeholders.

3.4.2 How would you approach improving the quality of airline data?
Discuss strategies for profiling, anomaly detection, and remediation. Highlight how you’d measure progress and automate checks.

3.4.3 Ensuring data quality within a complex ETL setup.
Describe your approach to monitoring, validation, and error handling in multi-step ETL pipelines. Address how you’d resolve data inconsistencies across sources.

3.4.4 Challenges of specific student test score layouts, recommended formatting changes for enhanced analysis, and common issues found in "messy" datasets.
Share your approach to data normalization, standardization, and handling missing values. Discuss how you’d automate recurring cleaning tasks.

3.4.5 Write a query to get the current salary for each employee after an ETL error.
Explain your method for reconstructing accurate records, auditing changes, and communicating risks to business stakeholders.

3.5 Presentation & Stakeholder Communication

Centro expects data engineers to present insights effectively and collaborate across technical and non-technical teams. You’ll be evaluated on your ability to tailor communication and visualize data for impact.

3.5.1 How to present complex data insights with clarity and adaptability tailored to a specific audience.
Discuss techniques for storytelling, visualization, and adjusting technical depth based on audience background.

3.5.2 Demystifying data for non-technical users through visualization and clear communication.
Share your approach to simplifying dashboards, using analogies, and making recommendations actionable.

3.5.3 Making data-driven insights actionable for those without technical expertise.
Explain how you bridge gaps between analytics and decision makers, focusing on clarity and relevance.

3.5.4 Designing a dynamic sales dashboard to track McDonald's branch performance in real-time.
Describe your process for understanding stakeholder needs, choosing KPIs, and iterating on dashboard design.

3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision that impacted business outcomes.
Focus on a situation where your analysis directly influenced a product change, cost savings, or performance improvement. Highlight the business context, your recommendation, and the measurable result.

3.6.2 Describe a challenging data project and how you handled it.
Choose a project with significant technical or stakeholder hurdles. Detail your approach to problem-solving, collaboration, and the outcome.

3.6.3 How do you handle unclear requirements or ambiguity in project scope?
Discuss your process for clarifying objectives, communicating with stakeholders, and iterating on solutions as new information emerges.

3.6.4 Tell me about a time when your colleagues didn’t agree with your approach. What did you do to address their concerns?
Share how you facilitated open discussion, presented evidence, and worked toward consensus or compromise.

3.6.5 Describe a time you had to negotiate scope creep when multiple teams kept adding requests. How did you keep the project on track?
Explain how you quantified new requests, communicated trade-offs, and used prioritization frameworks to maintain focus.

3.6.6 Give an example of how you balanced short-term wins with long-term data integrity when pressured to ship quickly.
Highlight how you communicated risks, made pragmatic decisions, and protected data quality for future analysis.

3.6.7 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Describe your strategy for building trust, using evidence, and aligning incentives with business goals.

3.6.8 Walk us through how you handled conflicting KPI definitions between teams and arrived at a single source of truth.
Show your method for reconciling differences, facilitating alignment, and documenting standardized metrics.

3.6.9 You’re given a dataset full of duplicates, nulls, and inconsistent formatting, with an urgent deadline. What do you do?
Outline your triage process, focus on high-impact fixes, and how you communicate data caveats to decision makers.

3.6.10 Tell me about a time you delivered critical insights even though a large portion of the dataset had missing values. What analytical trade-offs did you make?
Describe how you profiled missingness, chose suitable imputation or exclusion methods, and communicated uncertainty to stakeholders.

4.1 Company-specific tips:

Familiarize yourself with Centro’s mission to drive digital transformation in the banking and finance sector through open banking solutions. Understand how Centro leverages advanced data management and analytics to optimize internal processes and support strategic decisions. Research Centro’s modular approach to information systems, and be prepared to discuss how robust data engineering can enable innovation and transparency in financial services.

Learn about the Sella Group and Centro’s role within it, including their commitment to becoming a data-driven organization. Be ready to articulate how your skills as a data engineer align with Centro’s goals, especially in enabling business units to make informed decisions through reliable, scalable data platforms. Demonstrate awareness of the regulatory, compliance, and security requirements unique to banking and finance, and how these shape data engineering practices at Centro.

Be prepared to discuss your experience collaborating across multidisciplinary teams—such as business, IT, compliance, and risk—and how you’ve enabled these groups to optimize processes through innovative use of data. Centro values engineers who can translate technical solutions into business impact, so practice framing your technical achievements in terms of operational efficiency and strategic value.

4.2 Role-specific tips:

4.2.1 Master data pipeline design and ETL best practices for heterogeneous financial data.
Centro’s data engineering interviews often focus on designing scalable ETL pipelines that can ingest, transform, and distribute data from diverse sources. Practice explaining your approach to handling schema evolution, error handling, and automation for high-volume financial data. Be ready to discuss how you ensure data quality and reliability as new data sources or partners are integrated.

4.2.2 Demonstrate advanced SQL and Python proficiency for complex analytics and data modeling.
Expect to write SQL queries involving window functions, aggregations, and ranking, as well as Python scripts for data processing and automation. Prepare examples of optimizing queries for large datasets, handling missing or anomalous data, and designing data models that support both operational and analytical needs. Centro values engineers who can balance performance, scalability, and maintainability in their code.

4.2.3 Show expertise in cloud data platforms and scalable architecture (Microsoft, Oracle).
Centro’s data infrastructure relies heavily on cloud platforms, so highlight your hands-on experience with data storage, processing, and orchestration in environments like Microsoft Azure or Oracle Cloud. Be prepared to discuss your approach to system design, including partitioning strategies, fault tolerance, and scaling solutions to meet the demands of financial data workloads.

4.2.4 Illustrate your approach to data quality assurance and cleaning in high-volume environments.
Share real-world examples of profiling, cleaning, and validating data, especially in complex ETL setups. Discuss strategies for automation, reproducibility, and communicating limitations or risks to stakeholders. Centro values engineers who can maintain data integrity without sacrificing speed or scalability.

4.2.5 Practice communicating technical concepts clearly to non-technical stakeholders.
Centro’s data engineers frequently present insights to business, compliance, and risk teams. Prepare to explain complex data flows, quality issues, and analytical findings in accessible language. Use storytelling, visualization, and analogies to make your recommendations actionable and impactful for decision makers.

4.2.6 Prepare behavioral examples demonstrating cross-functional collaboration and adaptability.
Expect questions about managing ambiguous requirements, negotiating scope, and resolving conflicts between technical and business priorities. Practice sharing stories that highlight your ability to build consensus, drive alignment on KPIs, and deliver critical insights even under tight deadlines or with imperfect data.

4.2.7 Be ready to discuss system design for scalable, secure, and compliant data platforms.
Centro places a premium on designing data architectures that support evolving business needs while meeting regulatory requirements. Prepare to walk through the design of a data warehouse or platform for a financial use case, addressing scalability, data governance, security controls, and internationalization challenges.

4.2.8 Highlight your impact on business outcomes through data-driven solutions.
Centro seeks data engineers who can connect technical work to measurable business results. Prepare examples where your engineering decisions led to improved efficiency, cost savings, or better strategic insights. Quantify your impact and be ready to discuss how you prioritize long-term data integrity alongside short-term deliverables.

5.1 How hard is the Centro Data Engineer interview?
The Centro Data Engineer interview is moderately to highly challenging, with a strong focus on practical data pipeline design, advanced SQL and Python skills, and cloud platform expertise. You’ll need to demonstrate both technical depth and the ability to communicate complex concepts clearly to stakeholders in banking and finance. Candidates with hands-on experience building scalable, reliable data systems in regulated environments tend to excel.

5.2 How many interview rounds does Centro have for Data Engineer?
Centro typically conducts 5-6 interview rounds for Data Engineer roles. The process includes a recruiter screen, technical/case/skills assessment (often with a take-home assignment), behavioral interviews, and a final onsite round with multiple team members from engineering, analytics, and business functions.

5.3 Does Centro ask for take-home assignments for Data Engineer?
Yes, Centro often includes a take-home technical assignment as part of the interview process. These assignments focus on designing and implementing data pipelines, solving ETL problems, or writing SQL and Python code to address real-world data engineering scenarios relevant to financial services.

5.4 What skills are required for the Centro Data Engineer?
Key skills for Centro Data Engineers include advanced SQL and Python programming, expertise in data pipeline and ETL design, experience with cloud data platforms (Microsoft Azure, Oracle), strong data modeling abilities, and a deep understanding of data quality assurance. Communication skills and the ability to collaborate across multidisciplinary teams are also essential, especially in a regulated finance environment.

5.5 How long does the Centro Data Engineer hiring process take?
The typical Centro Data Engineer hiring process spans 3-5 weeks from initial application to final offer. Fast-track candidates may complete the process in as little as 2-3 weeks, while standard timelines allow about a week between each stage. Take-home assignments usually have a 2-3 day window for completion.

5.6 What types of questions are asked in the Centro Data Engineer interview?
Expect a mix of technical and behavioral questions, including data pipeline and ETL design challenges, complex SQL and Python coding problems, system architecture and data warehousing scenarios, data quality assurance strategies, and stakeholder communication cases. Behavioral questions often probe your experience collaborating with business, IT, and compliance teams, handling ambiguity, and driving data-driven outcomes.

5.7 Does Centro give feedback after the Data Engineer interview?
Centro typically provides high-level feedback through recruiters, especially after technical and onsite rounds. While detailed technical feedback may be limited, candidates usually receive information on their overall alignment with the role and areas for improvement.

5.8 What is the acceptance rate for Centro Data Engineer applicants?
Centro Data Engineer roles are competitive, with an estimated acceptance rate of 3-7% for qualified applicants. Demonstrating strong technical alignment, relevant financial data experience, and clear communication skills will help you stand out.

5.9 Does Centro hire remote Data Engineer positions?
Yes, Centro offers remote positions for Data Engineers, with some roles requiring occasional office visits for team collaboration. Flexible working policies are part of the compensation package, reflecting Centro’s commitment to supporting modern work arrangements.

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

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

With resources like the Centro 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!