Sr. Data Engineer - job 17 of 25

As a Sr. Data Engineer at Visa Technology & Operations LLC, your primary responsibilities include performing data engineering activities on the Hadoop and Greenplum Data Warehouse (GPDB) ecosystem, working with large data sets, and building programs that utilize MPP Data Warehouse platforms. You'll also be involved in loading data, documentation, performance testing, and handling long-term retention of security system logs.

To excel as a Sr. Data Engineer at Visa Technology & Operations LLC, candidates should possess strong technical skills in big data technologies, familiarity with Hadoop and GPDB, and experience with Massively Parallel Processing. A solid understanding of data architecture, regulatory compliance, and IT security requirements is crucial, along with excellent problem-solving abilities and a collaborative mindset.

The work environment for a Sr. Data Engineer at Visa Technology & Operations LLC in Austin is collaborative and dynamic. You’ll be part of a team that values idea-sharing and innovative solutions, allowing you to thrive in a community of like-minded professionals while tackling exciting data challenges.

Yes, the Sr. Data Engineer position at Visa Technology & Operations LLC offers the possibility of partial telecommuting. This flexibility allows you to maintain a work-life balance while contributing to impactful projects in data engineering.

In your role as a Sr. Data Engineer at Visa Technology & Operations LLC, you will play an essential part in ensuring IT security by being responsive to the evolving threat landscape, adhering to regulatory compliance, and managing long-term retention for security logs. Your expertise will help safeguard sensitive data and maintain industry standards.

When answering this question, be detailed about your hands-on experience with Hadoop and Greenplum. Highlight specific projects you've worked on, emphasizing the tools and technologies you used, any challenges you faced, and how you overcame them to achieve successful data engineering outcomes.

In response, discuss your debugging strategies, including systematic approaches to isolating issues, using log files, and performance testing. Share examples that showcase your problem-solving skills and your process in resolving data inconsistencies effectively.

Focus on your understanding of MPP and how it enables large-scale data processing. Share specific experiences where you've optimized data processing tasks, reduced run times, or improved overall data flow in your past projects by utilizing MPP platforms.

Highlight the key regulatory frameworks you’re knowledgeable about and how you've applied them in your previous roles. Emphasize your commitment to keeping your organization compliant while storing and processing sensitive data, and provide examples of how you've maintained these standards.

When crafting your answer, narrate the project’s goal, the diverse datasets involved, and the technologies used for integration. Discuss the challenges encountered and detail how you navigated through obstacles to complete the project successfully while meeting deadlines.

Discuss your methodologies for performance testing, including the tools you prefer, metrics you track, and how you analyze the results. Share a specific example demonstrating your ability to enhance system performance based on testing outcomes.

In your answer, mention specific data visualization tools you are proficient in, such as Tableau or Power BI. Explain how these tools help you convey complex data insights to stakeholders effectively, ideally with examples from your experience.

Emphasize the importance of teamwork in data engineering. Share your experiences communicating with team members, sharing strategies, and working through challenges together. Describe a specific project where collaboration led to success.

Focus on your data loading strategies, emphasizing tools, processes, and any ETL (Extract, Transform, Load) methodologies you've utilized. Provide examples that showcase your efficiency and thoroughness in ensuring data accuracy and reliability.

Mention your continuous learning habits, such as staying updated with industry news or enrolling in relevant courses. Share specific instances where adopting new technologies directly benefited your team's data engineering efforts.