Data Engineer - job 3 of 11

As a Data Engineer at Visa, your primary responsibilities include designing and deploying data pipelines, building data models, and supporting functional semantic data marts. You'll work closely with various teams to ensure high-quality data solutions, develop reports and dashboards, and utilize machine learning techniques. Your ability to interpret complex data and translate it into actionable insights is crucial in this role.

Visa requires candidates to hold a Bachelor's or Master's degree in computer or information science, along with relevant IT industry experience of 0.6 to 1.6 years. You'll need proven hands-on capability in developing data pipelines, familiarity with technologies like Hadoop and Spark, and fluency in programming languages like Python, Java, or Scala. If you love data and are eager to learn, we want you!

At Visa, Data Engineers should be well-versed in tools such as PySpark, SQL, Hadoop, Hive, and Spark. Knowledge of visualization tools like Tableau and Power BI is also beneficial. Additionally, familiarity with AI-centric libraries, machine learning algorithms, and data visualization techniques is advantageous for this role.

Collaboration is key for a Data Engineer at Visa. You'll interact with Data Analysts, Data Scientists, Software Engineers, and business stakeholders to design and implement data-driven solutions effectively. Being a team player and communicating effectively across functions is essential for driving innovation in our data projects.

Visa offers a vibrant work environment that fosters creativity and innovation. As a Data Engineer, you'll work on meaningful projects that have a real impact globally. Additionally, Visa values continuous learning, accountability, and customer focus, allowing you to grow in your career while contributing to uplifting communities through secure payment solutions.

In your response, detail specific projects where you built data pipelines, the tools and technologies you used, such as PySpark or SQL, and the outcomes of those projects. Highlight how your efforts led to efficient data processing or actionable insights for the business.

Discuss your experience creating dashboards and reports using tools like Tableau or Power BI. Explain the importance of visual storytelling in data analysis and provide an example of how your visualizations influenced decision-making within a previous role.

Be ready to discuss a specific instance where you faced a significant data challenge. Explain the problem, the steps you took to analyze it, and the technology stack you utilized. Make sure to emphasize the positive impact of your solution on the project or organization.

Illustrate your proficiency in machine learning by mentioning specific algorithms you have implemented in past projects. Discuss how you used libraries like TensorFlow or PyTorch to build models, the datasets you worked with, and any results achieved through your initiatives.

Explain your methodology for data testing and validation to ensure quality. Mention tools or techniques used to identify issues, as well as best practices for data cleaning and upkeep in your past projects.

Talk about your experience working within Agile frameworks, emphasizing how it improves project delivery and encourages collaboration. Share specific examples of Agile practices you've implemented to enhance team efficiency.

Highlight your familiarity with tools like Git explaining how you manage code versions in collaborative environments. Point out how version control practices have helped you maintain project integrity and facilitate team collaboration.

Discuss your approach to task prioritization, leveraging tools from Agile like stand-up meetings or Kanban boards, and how you kept your projects on track amidst changing requirements. Emphasize the importance of flexibility in this role.

Share your knowledge of various data modeling techniques, such as star schema or snowflake schema. Explain how these techniques have been useful in your previous roles in creating effective data warehouses or semantic data marts.

In your answer, express your excitement for Visa's mission and vision in shaping the payments industry. Align your career goals with the aspects of Visa that inspire you, such as their commitment to innovation, leadership, and customer focus.