ING Hubs Spain Data Engineer interview questions & guide 2026

The following questions are representative of the patterns observed in past ING Hubs Spain interviews. While the specific technical stack may vary by team, the core focus remains consistent: testing your ability to bridge the gap between complex data requirements and efficient engineering solutions.

Technical & Domain Expertise

These questions assess your foundational knowledge of data systems and your ability to optimize processes for scale and speed.

• How do you handle performance optimization in Apache Spark for large-scale data processing?
• Can you explain the differences and use cases for various data warehousing architectures?

Preparation should focus on demonstrating both your technical depth and your ability to articulate your thought process clearly. ING interviewers value candidates who can explain the "why" behind their technical decisions.

Role-related Knowledge – You must demonstrate a deep understanding of distributed systems and data modeling. Be prepared to discuss specific technologies like Spark, SQL, and cloud platforms, focusing on performance tuning and architectural trade-offs.

Problem-solving Ability – You will likely encounter practical challenges that require structured thinking. When solving coding or design problems, always state your assumptions, discuss potential edge cases, and explain the complexity of your proposed solution.

Communication & Stakeholder Management – As a Data Engineer, you act as a bridge between data producers and consumers. Show that you can explain complex technical concepts to non-technical stakeholders and that you prioritize business outcomes in your engineering work.

The interview process at ING Hubs Spain is designed to be thorough but efficient, typically moving from initial screening to technical validation and final behavioral assessments. You can expect a professional, fast-paced evaluation where communication is generally consistent. The process is designed to gauge your technical rigor, your ability to handle specific data challenges, and your alignment with the company’s collaborative work culture.

This timeline outlines the typical progression from your initial HR conversation to the final technical challenge. Use this to pace your preparation, ensuring you have refreshed your knowledge of Apache Spark, SQL, and system design principles before the technical rounds. Note that while the process is structured, the timing can vary based on team availability and the complexity of the specific role.

Technical Proficiency & Performance Optimization

This is the cornerstone of the interview. You will be evaluated on your ability to write efficient code and design systems that scale.

• Distributed Computing – Focus on Apache Spark internals, memory management, and shuffling.
• Database Engineering – Understanding indexing, partitioning, and query optimization in Oracle or similar RDBMS.
• ETL/ELT Pipelines – Expertise in orchestration, error handling, and idempotent data loads.

Problem-Solving & Coding

Beyond syntax, interviewers look for your ability to write maintainable, clean code under pressure.

• Coding Standards – Focus on readability, modularity, and proper documentation.
• Algorithmic Efficiency – Be prepared to discuss Time and Space complexity.
• Testing – Demonstrate how you validate your code against edge cases.

Example scenarios:

• "Given these data requirements, describe your approach to the schema design."
• "How would you handle a scenario where a data source fails mid-pipeline?"

As a Data Engineer, your primary objective is to build and maintain the infrastructure that turns raw data into actionable insights. You will be responsible for developing scalable ETL/ELT pipelines and ensuring that data is accessible, reliable, and secure. You will collaborate closely with Data Scientists and Analysts to understand their data requirements, translating those needs into robust technical specifications.

In addition to development, you will contribute to the ongoing improvement of the data platform. This includes automating manual processes, improving system monitoring, and participating in code reviews to maintain high quality across the team. You are expected to be a proactive problem-solver who can identify potential bottlenecks before they impact the business.

A successful candidate for this position will combine deep technical expertise with a strong collaborative spirit.

• Must-have skills: Proficient in Python or Scala, advanced SQL skills, and significant experience with Apache Spark.
• Nice-to-have skills: Experience with cloud-based data warehouses (e.g., Azure, AWS), containerization tools like Docker or Kubernetes, and familiarity with CI/CD practices.
• Experience: A proven track record of delivering data engineering projects in a professional environment, ideally within the financial or enterprise sector.

Q: How long does the entire interview process usually take? A: While it varies, candidates typically complete the process within 3 to 6 weeks. The initial HR screen is usually followed by a technical assessment and then a final round of interviews.

Q: Is the technical test difficult? A: The difficulty is generally considered average, but it is time-consuming. Success depends on your ability to meet all functional requirements while writing clean, efficient code.

Q: What is the best way to stand out? A: Focus on your practical experience. When discussing your past projects, explain the trade-offs you made and how your solution addressed a specific business problem.

Q: Is there a focus on specific technologies? A: Yes, Apache Spark, SQL, and ETL methodologies are central to the role. Be prepared to discuss these in depth regardless of the specific team you are interviewing with.

• Structure your answers: Use the STAR method (Situation, Task, Action, Result) for behavioral questions to keep your responses focused and impactful.
• Ask meaningful questions: At the end of the interview, ask about the team’s current technical challenges or their approach to data quality. It shows you are already thinking like an engineer on their team.
• Be honest about your experience: If you don't know an answer, it is better to explain how you would find the information or approach the problem logically rather than guessing.
• Follow up: If you haven't heard back within the expected timeframe, it is perfectly acceptable to send a professional follow-up email to your HR contact.

The Data Engineer position at ING Hubs Spain offers a unique opportunity to work on high-impact projects within a stable and innovative environment. By focusing on your core technical skills in Spark and SQL, and preparing clear examples of how you have solved complex data problems in the past, you will be well-positioned for success.

Remember that ING values both technical excellence and a collaborative mindset. Treat the interview as a two-way conversation; use the time to ensure that the role aligns with your own career goals. With focused preparation and a clear articulation of your experience, you can demonstrate exactly why you are the right fit for the team. Explore further resources on Dataford to refine your approach, and approach your interviews with confidence.

The salary module provides an overview of compensation benchmarks for this role. Use these figures as a guide for your expectations, keeping in mind that total compensation at ING Hubs Spain may also include benefits and performance-based incentives.