At a Glance
- Tasks: Design and build scalable data pipelines to support AI and data analytics.
- Company: Join Visa, a global leader in payments technology with a mission to uplift everyone.
- Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on innovation and quality.
- Why this job: Make a real impact on global transactions while developing your data engineering skills.
- Qualifications: 2-4 years of experience in data engineering with tools like SQL, Hive, and PySpark.
The predicted salary is between 55000 - 65000 £ per year.
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.
Job Description
Visa is accelerating the delivery of data analytics and AI powered products to support client growth and strategic decision‑making across regions. We are seeking a Data Engineer to execute on the design, delivery and evolution of scalable data engineering capabilities that underpin Data Science, AI and client facing products for all European markets. The role requires understanding and translating business needs into data models, creating robust data pipelines, and developing and maintaining databases. The candidate should be able to define and manage data load procedures, implement data strategies, and ensure robust operational data management systems. Collaborating with stakeholders across the organization to understand their data needs and deliver solutions is also a key part of this role. The ideal candidate will be proficient in big data tools like Hadoop, Hive, and Spark, programming languages such as Python and SQL and have strong analytical skills related to working with structured and unstructured datasets.
Primary Responsibilities
- Requirement Analysis: Understand and translate business needs into data models supporting long‑term solutions
- Build, manage and deploy large scale ETL processes to generate data assets for the region
- Build modular and reusable code considering the configurability and scalability while adhering to low-level design
- Perform thorough unit testing of development tasks and document the test results using standard defined templates
- Build, schedule, and manage DAGs in Apache Airflow efficiently
- Monitor data processing tasks using Airflow
- Ensure quality control of data assets, through monitoring and reconciling data loaded across different stages in the data pipeline
- Utilize strong data analytics skills to identify, discuss, and promptly fix data issues
- Apply debugging skills to quickly rectify execution errors, ensuring minimal delays and impact on business operations
- Collaborate and communicate with stakeholders for requirement understanding and clarifications
- Maintain the highest level of quality and detail-oriented approach in daily tasks
This is a hybrid position. This requires 3 days per week attendance in the London office.
Qualifications
Basic Qualifications
- 2-4 years development experience in building data pipelines and writing ETL code using Hive, PySpark, SQL and Unix
- Experience in writing and optimizing SQL queries in a big data environment
- Experience working in Linux/Unix environment and exposure to command line utilities
- Experience creating/supporting production software/systems and a proven track record of identifying and resolving performance bottlenecks for production systems
- Exposure to code version control systems (e.g. git, GitHub)
- Experience working with cloud services (e.g. AWS, GCP, Azure)
- Familiarity with common agentic coding tools
- Hands‑on experience building GenAI‑based applications or workloads
- Ability to understand a diverse set of business domains and requirements
- Good understanding of agile working practices and related program management skills
- Experience with workflow orchestration tools (e.g., Apache Airflow) and designing reliable data workflows
- Experience applying data quality frameworks and practices (e.g., automated checks, reconciliation and data observability)
- Strong communication and presentation skills with ability to interact with different cross‑functional team members at varying levels
Preferred Qualifications
- Advanced degree in technical field (e.g. Computer Science, statistics, etc.)
- Experience with visualization tools like Tableau and Power BI
- Exposure to Financial Services or the Payments Industry
- Hands‑on experience with CI/CD and automation pipelines (e.g., GitHub Actions, Jenkins, Azure DevOps) including testing and release practices
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.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer
✨Get Involved in Data Science Meetups
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We think you need these skills to ace Data Engineer
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Tink, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Tink. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Tink
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Tink!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.