A Data Engineer at UBS plays a critical role in shaping the financial technology landscape. As one of the world's largest wealth managers and investment banks, UBS relies on massive volumes of data to drive trading decisions, manage risk, meet strict regulatory compliance standards, and deliver personalized digital experiences to global clients. In this role, you are responsible for building and maintaining the robust data architectures that make these operations possible.

The impact of a Data Engineer is visible across multiple business units. Whether you are optimizing low-latency data pipelines for investment banking, integrating complex financial data streams for asset management, or scaling cloud-based analytics platforms, your work directly influences the firm's strategic capabilities. UBS is actively migrating legacy systems to modern cloud environments, which means you will work with cutting-edge technologies like Databricks, PySpark, and cloud-native databases to process structured and unstructured financial data at scale.

This role requires a unique blend of deep technical expertise and financial domain awareness. You will not only write optimized code to process terabytes of data but also ensure that your pipelines are secure, highly available, and compliant with international banking regulations. It is a challenging yet rewarding environment where engineering excellence meets global financial strategy.

To succeed in the UBS hiring process, you must be prepared for a variety of technical, practical, and scenario-based questions. The questions you face will test your foundational coding skills, your understanding of big data frameworks, and your ability to design resilient systems.

The following questions are grouped by core categories to help you identify patterns and structure your preparation.

Coding & Big Data Frameworks

These questions evaluate your hands-on coding ability in Python, SQL, and PySpark, focusing on data manipulation and API usage.

• How do you use the PySpark DataFrame API to handle missing or null values in a large dataset?

Preparing for an interview at UBS requires a balanced approach. You must demonstrate strong technical capabilities while also showing that you can work effectively within a highly regulated, collaborative global team.

Your interviewers will evaluate you against several core criteria to determine your suitability for the Data Engineer position.

Role-Related Knowledge – You must show a deep, conceptual understanding of data engineering principles, including data modeling, ETL/ELT design, and distributed computing. Interviewers will look for hands-on familiarity with tools like Databricks, PySpark, and relational databases.

Problem-Solving & System Design – You need to demonstrate a structured approach to solving complex, ambiguous problems. When designing systems, you must think about scalability, security, cost efficiency, and performance optimization.

Operational Resilience – In a financial institution, system downtime or data inaccuracies can have severe consequences. You must show that you prioritize fault tolerance, robust error handling, monitoring, and automated recovery in your pipeline designs.

Collaboration & Communication – UBS operates globally, meaning you will work with diverse teams across different time zones. You must be able to explain complex technical concepts clearly to both technical peers and business stakeholders.

The interview process for a Data Engineer at UBS is structured to thoroughly evaluate both your practical coding skills and your high-level system design capabilities. The process typically begins with an automated online assessment before progressing to live technical discussions and final behavioral evaluations.

The journey starts with an online coding challenge, usually hosted on platforms like HackerRank. This initial screen focuses on testing your core programming skills in Python, your ability to manipulate data using the PySpark DataFrame API, and your proficiency in writing clean SQL queries. Candidates who pass the online assessment are invited to technical rounds.

The live technical rounds are typically conducted by senior engineers or technical architects. These rounds go deep into conceptual data engineering questions, pipeline architecture, and scenario-based problem-solving. Finally, a behavioral and HR round assesses your cultural fit, communication style, and alignment with UBS values.

This visual timeline illustrates the typical progression from the initial online test to the final hiring decision. Candidates should expect the technical rounds to be highly conversational, with interviewers asking follow-up questions to test the depth of your knowledge. While the process is rigorous, thorough preparation on core data engineering concepts will help you navigate each stage with confidence.

To stand out in the UBS interview process, you need to understand the key technical domains where candidates are evaluated most heavily.

Big Data Processing with PySpark & Databricks

A significant portion of the technical evaluation focuses on your ability to process large datasets efficiently. Because UBS relies on modern lakehouse architectures, proficiency with distributed computing is essential.

Be ready to go over:

• DataFrame API Operations – Writing optimized transformations, aggregations, and filtering operations.
• Performance Tuning – Managing data shuffling, partitioning, bucketing, and leveraging caching strategies.
• Databricks Environment – Utilizing notebooks, managing clusters, and understanding Delta Lake features like ACID transactions and time travel.
• Advanced concepts (less common) – Custom User Defined Functions (UDFs) optimization, PySpark memory management tuning, and integrating PySpark with streaming sources.

Example questions or scenarios:

• "How would you optimize a PySpark job that is experiencing severe data skew during a join operation?"
• "Explain how Delta Lake ensures data consistency in a multi-writer environment."

SQL & Database Design

Relational databases and data warehouses remain core to financial systems. You must demonstrate a strong command of relational algebra, database design, and query optimization.

Be ready to go over:

• Complex Querying – Using window functions, common table expressions (CTEs), and advanced joins.
• Data Modeling – Designing star schemas, snowflake schemas, and understanding normalization versus denormalization.
• Query Optimization – Analyzing execution plans, indexing strategies, and identifying bottlenecks in slow-running queries.
• Advanced concepts (less common) – Database partitioning strategies, transaction isolation levels, and materialized view management.

Example questions or scenarios:

• "Write a query to find the top three highest-value transactions for each customer account over the last month."
• "How would you design a schema to store historical exchange rates where rates can change multiple times a day?"

Pipeline Orchestration & Fault Tolerance

Building a pipeline is only half the battle; ensuring it runs reliably in production is what separates junior engineers from senior professionals.

Be ready to go over:

• Orchestration Tools – Understanding how to schedule, monitor, and manage workflows using tools like Apache Airflow or Azure Data Factory.
• Error Handling & Recovery – Designing pipelines that can gracefully handle source system failures, network drops, or malformed data.
• Data Quality & Governance – Implementing automated data validation checks and maintaining data lineage.
• Advanced concepts (less common) – Implementing self-healing pipelines, dynamic DAG generation, and managing infrastructure as code (IaC) for data pipelines.

Example questions or scenarios:

• "Walk me through your strategy for handling a critical API failure mid-way through a daily batch loading process."
• "How do you ensure that sensitive financial data is masked or encrypted as it flows through your data pipelines?"