Graduate Data Engineer

About Us
Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid.

At Visa, you'll have the opportunity to create impact at scale — tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world.

Join Visa and do work that matters – to you, to your community, and to the world. Progress starts with you.

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Job Description

As a Data Engineering Graduate, you will play a key role in building the scalable data foundation that powers Visa's analytical, data science, and product development work across Europe. Unlike traditional Data Science roles focused on modelling or insight generation, Data Engineering focuses on developing data pipelines, transforming large datasets, automating processes, and enabling teams to operate efficiently and at scale. 

You'll work closely with Data Engineers and Data Scientists to build repeatable, reusable, and automated data assets that support client projects across Europe. Much of the work is backend-focused, requiring strong coding foundations and an engineering-first mindset. The team works in an AI-native way: engineers are expected to use AI coding as part of their everyday workflow, not as a novelty, to move faster on pipeline development, testing, and documentation while keeping full ownership of code quality and review. The role is increasingly a blend of Data and AI Engineering: beyond traditional pipelines, you will also help design and build AI agents and agentic workflows that automate parts of the data and analytics lifecycle itself. While the role is not typically client-facing, strong communication is required to work effectively in a global team environment. 

What You'll Do 

As part of your graduate role, you will: 

• Develop a deep understanding of Visa's data ecosystem and how data is used across Data Science, Product, and Analytics teams. 

• Build, test, and maintain ETL/ELT data pipelines using Python, SQL, Spark, and other big-data tools. 

• Use AI coding assistants (e.g., Claude Code, GitHub Copilot) throughout the development lifecycle - drafting pipeline code, generating and reviewing tests, debugging, and writing documentation as part of an AI-native, engineering-first workflow. 

• Design, build, and maintain AI agents and agentic workflows (e.g., automated data-quality checks, pipeline troubleshooting assistants, orchestration or documentation agents) that extend what traditional ETL/ELT pipelines can do on their own. 

• Support the design and delivery of scalable, reusable data assets to be used repeatedly across client engagements. 

• Automate and standardize backend data workflows, including DAG-based orchestration (e.g., Airflow), to improve the reliability and consistency of data used by downstream teams. 

• Work with large datasets, performing transformations and ensuring data quality, integrity, and accuracy. 

• Collaborate with global Data Engineering teams (North America & other regions) on productionizing and maintaining key data assets. 

• Participate in code reviews, contribute to technical documentation, and follow engineering best practices including Git-based version control and CI/CD - including how AI-assisted contributions are reviewed and attributed. 

• Troubleshoot data pipeline issues and assist with performance optimization. 

The annual salary for a new hire in this position is 148 675 PLN. In addition, this position may be eligible for bonus, equity, and a competitive benefits package.

Qualifications

Basic Qualifications 

• Graduating in 2027 with a Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, Mathematics, Engineering, or another technical field, looking to start working in October 2027.

• Strong programming foundations in Python and SQL. 

• Interest in backend engineering, data systems, and scalable data processing. 

• Familiarity with at least one big-data or distributed computing framework (e.g., Spark, Hadoop, Hive). 

• Strong analytical thinking and problem-solving skills. 

• Ability to communicate clearly in English, both written and verbal. 

Preferred Qualifications 

Experience or exposure to: 

• Data pipeline development (ETL/ELT). 

• Git or other version control systems. 

• Workflow orchestration tools, particularly Airflow (used extensively for pipeline orchestration), as well as Luigi or Prefect. 

• Using AI coding assistants (e.g., Claude Code, GitHub Copilot, Cursor) to accelerate development, testing, and debugging — comfort with prompt-based code generation and reviewing AI-suggested code critically. 

• Building or experimenting with AI agents or agentic workflows (e.g., LLM-based automation, tool-calling agents, multi-agent orchestration, MCP servers) — academic projects, hackathons, or personal projects all count. 

• Cloud platforms (AWS, GCP, Azure) and/or Databricks — helpful but not required; most of our current pipeline work runs on on-prem big-data infrastructure (Spark, Hive, Trino), so cloud/Databricks exposure is a bonus, not a prerequisite. 

• Unix/Linux command line. 

• Interest in scaling data engineering processes and learning software engineering best practices, including how AI tooling fits into a disciplined engineering workflow. 

• Ability to collaborate effectively with teammates across geographies. 

Please submit your CV in English. 

Visa is an EEO Employer

Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law.

Visa welcome and encourages application from persons with disabilities. Accommodations are available on request for candidates taking part in all aspects of the selection process.