What is a Data Engineer at BBVA?

As a Data Engineer at BBVA, you are at the forefront of our digital transformation, building the foundational data infrastructure that powers one of the most innovative global financial institutions. Your work directly impacts how we process millions of daily transactions, assess risk, and deliver personalized financial products to our customers across Latin America, Europe, and beyond. You are not just moving data; you are enabling the intelligence that drives modern banking.

This role requires a unique balance of massive scale and rigorous precision. You will be responsible for designing, building, and optimizing robust data pipelines that feed into our advanced analytics, machine learning models, and real-time reporting dashboards. Because you are handling highly sensitive financial data, your solutions must be highly performant, exceptionally secure, and fully compliant with international banking regulations.

You can expect to collaborate closely with data scientists, product owners, and software engineering teams to solve complex, high-stakes challenges. Whether you are modernizing legacy on-premise systems to cloud-native architectures or optimizing real-time streaming pipelines for fraud detection, the work is technically demanding but incredibly rewarding. You will be a critical pillar in BBVA's mission to bring the age of opportunity to everyone through data-driven innovation.

Getting Ready for Your Interviews

Preparing for your Data Engineer interviews at BBVA requires a strategic approach that balances deep technical knowledge with an understanding of our corporate culture. You should be ready to demonstrate not only your coding and architectural skills but also your ability to operate in a highly regulated, collaborative environment.

• Technical Proficiency – Interviewers will evaluate your hands-on ability to write optimized code, design scalable data architectures, and build resilient pipelines. You can demonstrate strength here by confidently discussing your experience with SQL, big data frameworks, and cloud platforms, while clearly explaining your design trade-offs.
• Problem-Solving Ability – We look for candidates who can take ambiguous business requirements and translate them into structured data solutions. Show your strength by walking interviewers through your analytical process, highlighting how you troubleshoot data bottlenecks and ensure data quality.
• Domain Awareness – Working at BBVA means operating within the financial sector, where security, governance, and compliance are paramount. You will stand out if you show an understanding of how to handle sensitive data securely and design pipelines that maintain strict auditability.
• Culture Fit and Collaboration – We value team players who communicate clearly and thrive in cross-functional environments. Be prepared to share examples of how you have collaborated with diverse stakeholders, navigated shifting priorities, and contributed to a positive team dynamic.

Interview Process Overview

The interview process for a Data Engineer at BBVA is designed to be thorough, technically rigorous, but ultimately very cordial and welcoming. Candidates consistently report that our interviewers go out of their way to make you feel comfortable, allowing you to showcase your true capabilities. The entire end-to-end process typically takes about a month, so patience and consistent engagement are key to your success.

Your journey will generally begin with an initial screening with our Human Resources team. During this stage, HR will explain the general policies of the bank, outline the day-to-day tasks of the role, and discuss high-level compensation and benefits. Following this, you will progress to technical interviews with the area managers and senior engineers. These sessions will dive deep into your technical capabilities, architectural thinking, and problem-solving skills through practical scenarios.

While the technical rounds are challenging and fun, be aware that the administrative stages can sometimes move slowly. If you are selected, the final offer generation and documentation phase can take over two weeks to finalize. We encourage you to stay in touch with your recruiter and use this time to prepare for your eventual onboarding.

This timeline illustrates the typical progression from your initial HR screening through the technical rounds and finally to the offer stage. You should use this visual to pace your preparation, focusing heavily on your technical and system design skills after passing the initial behavioral screen. Note that timelines can vary slightly depending on your specific region, such as Mexico City or Buenos Aires, but the core sequence remains consistent.

Deep Dive into Evaluation Areas

Data Architecture and Pipeline Engineering

As a Data Engineer, your primary responsibility is moving and transforming data efficiently and reliably. Interviewers will heavily evaluate your ability to design robust ETL (Extract, Transform, Load) and ELT pipelines. Strong performance in this area means you can design architectures that scale, recover gracefully from failures, and ensure high data fidelity.

Be ready to go over:

• Batch vs. Streaming Processing – Understanding when to use scheduled batch jobs versus real-time streaming, and the tools associated with each.
• Data Modeling – Designing schemas (e.g., Star, Snowflake) that optimize for both storage costs and analytical query performance.
• Orchestration – Managing complex dependencies using tools like Apache Airflow or similar enterprise schedulers.
• Advanced concepts (less common) –
  • Change Data Capture (CDC) implementation.
  • Idempotent pipeline design.
  • Handling late-arriving data in distributed systems.

Example questions or scenarios:

• "Walk me through a time you had to design a pipeline to ingest millions of daily transactional records. How did you ensure no data was duplicated?"
• "How would you design an architecture to process real-time credit card swipes for fraud detection?"
• "Explain how you handle schema evolution in a long-running data pipeline."

SQL and Database Optimization

SQL remains the lingua franca of data engineering, and at BBVA, you will be tested on your ability to write complex, highly optimized queries. It is not enough to simply retrieve data; you must understand how the database engine executes your query. A strong candidate will naturally discuss indexing, execution plans, and partitioning strategies.

Be ready to go over:

• Advanced SQL Functions – Mastery of window functions, CTEs (Common Table Expressions), and complex joins.
• Performance Tuning – Identifying bottlenecks in slow-running queries and optimizing them through indexing or query refactoring.
• Data Warehousing – Understanding the architectural differences between transactional databases (OLTP) and analytical warehouses (OLAP).
• Advanced concepts (less common) –
  • Query execution plan analysis.
  • Materialized views and their trade-offs.
  • Handling skewed data in distributed joins.

Example questions or scenarios:

• "Given a table of customer transactions, write a query to find the top 3 spending customers in each region over the last 30 days."
• "You have a query that is taking hours to run on a massive historical table. What steps do you take to optimize it?"
• "Explain the difference between a clustered and non-clustered index, and when you would use each."

Big Data and Cloud Technologies

BBVA leverages modern cloud ecosystems and big data frameworks to handle our massive data footprint. You will be evaluated on your familiarity with distributed computing and cloud-native data services. A strong performance demonstrates hands-on experience with these tools and an understanding of their underlying mechanics.

Be ready to go over:

• Distributed Computing – Experience with Apache Spark, Hadoop, or similar frameworks for processing large-scale datasets.
• Cloud Infrastructure – Familiarity with AWS, GCP, or Azure data services (e.g., S3, Redshift, BigQuery, Databricks).
• Data Governance and Security – Implementing role-based access control and data encryption within cloud environments.
• Advanced concepts (less common) –
  • Spark memory management and tuning (e.g., handling OutOfMemory errors).
  • Infrastructure as Code (Terraform, CloudFormation).
  • Serverless data architectures.

Example questions or scenarios:

• "Describe a scenario where your Spark job was failing due to data skew. How did you diagnose and resolve the issue?"
• "Compare the advantages of using a cloud data warehouse versus an on-premise Hadoop cluster."
• "How do you ensure that personally identifiable information (PII) is securely masked in your cloud storage buckets?"

Behavioral and Cultural Fit

Technical brilliance must be matched with strong communication and teamwork. We evaluate how you respond to feedback, navigate ambiguity, and align with BBVA's core values. Strong candidates provide structured, compelling narratives about their past experiences using frameworks like the STAR method (Situation, Task, Action, Result).

Be ready to go over:

• Cross-functional Collaboration – Working effectively with non-technical stakeholders to define data requirements.
• Adaptability – Pivoting your approach when business priorities change or technical roadblocks arise.
• Ownership – Taking responsibility for the end-to-end lifecycle of your data products, including maintenance and monitoring.
• Advanced concepts (less common) –
  • Mentoring junior engineers.
  • Leading a complex technical migration.
  • Managing vendor relationships for data tools.

Example questions or scenarios:

• "Tell me about a time you had to explain a complex data architecture to a non-technical product manager."
• "Describe a situation where a pipeline you built failed in production. How did you handle the communication and the fix?"
• "Give an example of a time you disagreed with a team member on a technical design. How did you resolve it?"

Key Responsibilities

As a Data Engineer at BBVA, your day-to-day work revolves around building and maintaining the critical infrastructure that allows our business to make data-driven decisions. You will be actively involved in designing, developing, and deploying scalable data pipelines that extract data from diverse internal systems, transform it according to business rules, and load it into centralized analytical environments. This requires a constant focus on data quality, ensuring that the information downstream users rely on is accurate, timely, and complete.

Collaboration is a massive part of your daily routine. You will work side-by-side with data scientists to prepare datasets for machine learning models, and with product managers to ensure reporting dashboards have the underlying data they need. You will also partner closely with software engineering teams to integrate data collection directly into customer-facing applications, ensuring a seamless flow of information from the user's mobile app directly into our data lakes.

Additionally, you will play a key role in modernization initiatives. Many of our teams are actively migrating legacy on-premise workloads to modern cloud architectures. You will be responsible for refactoring old processes, optimizing legacy SQL scripts, and implementing modern CI/CD practices for data pipelines. Through all of this, you will maintain strict adherence to BBVA's security and governance protocols, protecting our customers' data at every step.

Role Requirements & Qualifications

To thrive as a Data Engineer at BBVA, you need a solid foundation in software engineering principles combined with specialized knowledge of data systems. We look for candidates who can balance rapid development with the rigorous quality standards required in the banking sector.

• Must-have technical skills – Advanced proficiency in SQL and at least one programming language (Python, Scala, or Java). You must have hands-on experience building ETL/ELT pipelines and working with relational databases.
• Must-have experience – Typically, 3+ years of professional experience in data engineering, software engineering, or a closely related technical field. You should have a proven track record of deploying code to production environments.
• Nice-to-have technical skills – Experience with distributed computing frameworks (Apache Spark, Kafka), cloud platforms (AWS, GCP, Azure), and orchestration tools (Airflow). Familiarity with financial data models is a significant plus.
• Soft skills – Strong analytical thinking, clear communication, and the ability to manage expectations with business stakeholders. You must be comfortable working in an agile environment and adapting to shifting priorities.

Common Interview Questions

The questions below are representative of what candidates typically face during the Data Engineer interview process at BBVA. While your specific questions may vary depending on the team and your location, reviewing these patterns will help you understand the depth and focus of our technical evaluations.

Technical and SQL Challenges

These questions test your hands-on ability to manipulate data, optimize queries, and solve common data engineering problems using code.

• Write a SQL query to calculate a 7-day rolling average of transaction volumes per user.
• How do you optimize a SQL query that involves joining two massive tables?
• Write a Python script to parse a large JSON file, extract specific fields, and load them into a relational database.
• Explain the difference between a left join, an inner join, and a full outer join, and provide a use case for each.
• How do you handle duplicate records in a dataset without a primary key?

System Design and Architecture

These questions evaluate your ability to think at a macro level, design scalable systems, and make appropriate trade-offs between different technologies.

• Design a data pipeline to ingest daily exchange rates from multiple third-party APIs and make them available for real-time querying.
• If you were building a data warehouse from scratch, how would you choose between a Star schema and a Snowflake schema?
• How would you design a system to handle late-arriving events in a daily batch processing pipeline?
• Explain your strategy for implementing data quality checks across a multi-stage ETL process.
• Walk me through how you would migrate an on-premise SQL Server database to a cloud data warehouse with zero downtime.

Behavioral and Experience

These questions focus on your past experiences, your problem-solving methodology, and your alignment with the collaborative culture at BBVA.

• Tell me about the most complex data pipeline you have ever built. What made it complex?
• Describe a time when you had to push back on a stakeholder's request because it was not technically feasible.
• How do you stay updated with the rapidly changing landscape of data engineering tools?
• Tell me about a time you discovered a critical bug in your data pipeline after it went to production. What did you do?
• Describe your experience working in an agile team. How do you handle sprint planning and technical debt?

Frequently Asked Questions

Q: How long does the interview process typically take? The entire process generally takes about a month from the initial HR screen to the final decision. Please note that if you are selected, the administrative process of generating the final offer and collecting documentation can sometimes take over two weeks.

Q: What is the compensation structure like for this role? BBVA offers a highly competitive benefits package, including excellent banking perks, health coverage, and retirement plans. While base salaries are generally aligned with the market median, the comprehensive benefits and job stability make the overall compensation package very attractive.

Q: Are the interviews highly difficult? Candidates generally rate the difficulty as average to difficult. The technical rounds are rigorous and will test your practical capabilities, but our interviewers strive to create a collaborative and cordial environment where you can comfortably demonstrate your skills.

Q: Do I need prior experience in the banking or financial sector? While prior experience in finance is a plus and will help you understand our data models faster, it is not strictly required. We are primarily looking for strong core data engineering skills and a willingness to learn our specific domain and compliance standards.

Q: What should I expect during the HR screening? The HR screening is generally straightforward and cordial. They will explain the daily tasks of the role and the bank's general policies. However, be prepared that they might not have deep technical details about the role or highly specific nuances about certain medical benefits on hand.

Other General Tips

• Patience is a Virtue: The hiring process at BBVA, especially the final offer stage, can be lengthy due to internal approvals. Do not let a few weeks of silence discourage you; maintain polite contact with your recruiter.
• Focus on Security and Compliance: Because we are a bank, data governance is not an afterthought. Incorporate concepts like data masking, encryption, and auditability into your system design answers to impress your interviewers.
• Clarify Before Designing: When given an architectural scenario, take a moment to ask clarifying questions about data volume, latency requirements, and the end-user before you start drawing on the whiteboard.
• Master the Fundamentals: While buzzwords like AI and real-time streaming are exciting, the bulk of our technical evaluation will focus on your mastery of core fundamentals: SQL optimization, Python scripting, and solid data modeling.

Summary & Next Steps

Joining BBVA as a Data Engineer is a unique opportunity to build systems that operate at a massive scale while directly impacting the financial well-being of millions of people. The challenges you will face here—from modernizing legacy systems to building real-time fraud detection pipelines—will push you to grow technically and professionally. By preparing thoroughly for both the technical rigor and the collaborative nature of our teams, you will position yourself as a standout candidate.

Focus your preparation on mastering SQL optimization, articulating clear system designs, and demonstrating your hands-on experience with modern data pipelines. Remember that our interviewers are looking for colleagues they can trust and collaborate with, so approach the conversations with confidence, curiosity, and a positive attitude. The process may be lengthy, but the reward is a stable, impactful career at a leading global institution.

The salary module above provides an overview of the compensation landscape for this role based on market data and candidate reports. Use this information to understand the typical base salary ranges and how they combine with our strong benefits package to form your total compensation.

You have the skills and the potential to succeed in this process. Continue refining your technical narratives, practice your system design frameworks, and explore additional interview insights and resources on Dataford to round out your preparation. Good luck!