Staff Data Engineer - job 6 of 50

As a Staff Data Engineer at Visa in Atlanta, you will be responsible for leading and mentoring a team of data engineers. Your role includes managing and prioritizing projects through collaboration with other teams. You'll also be tasked with driving the strategy and execution of key data engineering initiatives, designing robust data pipelines, and implementing data solutions that meet business needs.

Qualifications for the Staff Data Engineer position at Visa include proficiency in big data tools like Hadoop, Hive, and Spark, alongside strong programming skills in Scala, Python, and SQL. Additionally, you should have experience with data modeling, data integration, and operational data management systems, alongside the ability to lead a team effectively.

Collaboration is key in the Staff Data Engineer role at Visa as you will work closely with various stakeholders across the organization to understand their data needs. This will involve delivering tailored data solutions and ensuring that projects align with broader business objectives, fostering innovation and efficiency within data initiatives.

Essential skills for success in the Staff Data Engineer position at Visa include strong analytical capabilities, expertise in data engineering principles and practices, and proficiency in handling both structured and unstructured datasets. Furthermore, effective communication and leadership are vital to guide your team and manage stakeholder expectations.

Yes, the Staff Data Engineer position at Visa is a hybrid role, allowing for a combination of in-office and remote work. The specific expectations regarding in-office days will be confirmed by your hiring manager, providing flexibility while ensuring team cohesion.

In your response, highlight specific projects where you designed data pipelines. Mention the technologies used and the impact of these pipelines on data accessibility and reliability within your organization.

Share a specific example of a data modeling project, detailing the business challenge, your approach to translating needs into models, and the final outcome. Highlight how your contributions improved data quality or business decision-making.

Discuss your methods for evaluating project urgency and impact. Include any frameworks or criteria you use to prioritize work, along with how you communicate those priorities to your team.

Describe your proficiency with big data tools like Hadoop, Hive, and Spark. Provide examples of how you've effectively utilized these tools in past projects, demonstrating your practical know-how.

Explain your approach to defining data quality criteria and the processes you implement to reject or discard data that doesn't meet these standards. Include examples of how you have tackled data quality concerns in your work.

Share specific strategies you employ when mentoring junior engineers, such as providing constructive feedback, setting clear goals, and facilitating hands-on learning opportunities to help them grow their skills.

Provide a specific scenario where you worked with stakeholders to identify their data needs. Discuss how you engaged them throughout the process and ensured that the final solutions met their expectations.

Identify a particular challenge in your data engineering experience and describe the steps you took to resolve it. Highlight your problem-solving skills and ability to adapt.

Discuss your strategies for professional development that include following relevant blogs, attending webinars, or participating in industry conferences. Highlight how you apply new knowledge to your work.

Explain your understanding and experience with implementing CI/CD pipelines in data solutions. Illustrate the benefits this brings to data management and delivery in your projects.