Staff Data Engineer - job 14 of 50

As a Staff Data Engineer at Visa Technology & Operations LLC, your key responsibilities include creating new capabilities for our AI Platform, maturing the existing code base, and refactoring functionalities for better performance. You'll collaborate closely with cross-functional teams including Product Managers and Software Engineers to deliver high-quality, data-driven solutions on time. Your role also involves investigating new requirements, managing risks, and communicating technical solutions in business terms, ensuring alignment with the company's architectural vision.

To excel in the Staff Data Engineer role at Visa Technology & Operations LLC, candidates should have a strong background in Data Engineering, proficient in programming languages such as Python or Java, and experienced with database technologies like SQL and NoSQL. A solid understanding of AI and machine learning principles is beneficial. The ideal candidate should also possess excellent collaboration and communication skills to work effectively with various teams.

Yes, Visa Technology & Operations LLC offers flexibility for the Staff Data Engineer role, allowing for partial telecommuting. This enables team members to maintain a work-life balance while still engaging closely with their Austin-based colleagues and participating in collaborative projects.

Collaboration is key for the Staff Data Engineer at Visa Technology & Operations LLC. You will work alongside Product Managers, Architects, Analysts, and other Software Engineers to ensure the development and implementation of efficient data-driven business solutions. This collaborative environment not only enhances innovation but also ensures that projects meet deadlines and adhere to business requirements.

At Visa Technology & Operations LLC, projects for a Staff Data Engineer primarily focus on enhancing our AI Platform by developing new capabilities and maturing the existing code base. You will refactor existing functionalities for better performance and maintainability, ensuring that our solutions are both innovative and aligned with business needs.

When answering this question, focus on specific projects where you have designed or optimized data pipelines. Discuss the technologies you used, such as Apache Kafka or AWS Glue, and highlight any challenges you overcame while ensuring the flow of data was efficient and reliable.

A good response would include examples of risk management strategies you've used, such as regular status check-ins, documentation of potential issues, and contingency planning. Emphasize your proactive approach to identifying and mitigating risks to ensure project timelines and quality are upheld.

Focus on your knowledge of data validation methodologies and tools you've used, such as unit tests or automated data quality frameworks. Provide specific examples of how you have implemented these techniques in previous projects to maintain the accuracy and reliability of the data.

When answering this question, narrate a specific challenge, detailed steps you took to troubleshoot and resolve the issue, and any tools or methods you utilized. Highlight the positive outcome resulting from your problem-solving abilities.

Mention any specific resources such as blogs, webinars, or conferences you participate in, alongside your community involvement or networking efforts with other data professionals. This shows your commitment to continuous learning in the field.

Discuss your proficiency in both SQL and NoSQL database technologies, providing examples of projects where you successfully utilized their features. Mention scenarios where you chose one over the other based on project requirements or performance considerations.

Provide a structured approach to communication that includes using clear, non-technical language, incorporating visual aids, and focusing on the business impact of the technical solution. Share an instance where this approach led to successful understanding and approval of a project.

In your response, list programming languages relevant to the role, such as Python or Java, and include specific applications in your past projects. Highlight any frameworks or libraries that enhanced your data processing and manipulation.

Explain the significance of documentation in ensuring project continuity, team collaboration, and code maintainability. Share examples of how comprehensive documentation has benefitted past projects you’ve worked on.

Detail your troubleshooting process, such as analyzing logs, identifying bottlenecks, and benchmarking system performance. Share specific instances where your strategies successfully improved system efficiency.