Sr. Data Engineer - job 14 of 29

As a Sr. Data Engineer at Visa Technology & Operations LLC, you will be responsible for performing data engineering tasks on Hadoop and Greenplum Data Warehouse ecosystems. This includes working with large datasets, developing programs for Massively Parallel Processing Data Warehouse platforms, and leveraging Big Data technologies collaboratively. You'll also manage data loading from multiple sources, performance testing, debugging applications, and ensuring adherence to IT security requirements.

To apply for the Sr. Data Engineer role at Visa Technology & Operations LLC, candidates typically need a bachelor's degree in Computer Science, Engineering, or a related field. Additionally, substantial experience with Hadoop, GPDB, and Big Data technologies is essential. Strong skills in problem-solving, performance testing, and an understanding of IT security compliance will also be important.

Visa Technology & Operations LLC prioritizes security and compliance in its data systems. As a Sr. Data Engineer, you will be involved in ensuring that data handling complies with evolving regulatory requirements and that IT security standards are maintained. You'll work on managing the long-term retention of critical security logs, which plays a key role in safeguarding the integrity of our systems.

For the Sr. Data Engineer role at Visa Technology & Operations LLC, proficiency in Hadoop and Greenplum Data Warehouse (GPDB) is essential. Familiarity with Big Data technologies, programming languages such as Python or Java, and experience with techniques related to Massively Parallel Processing will give you a significant advantage in this position.

Yes, the Sr. Data Engineer position at Visa Technology & Operations LLC may allow for partial telecommuting. While the role is primarily based in the Austin, TX office, the company recognizes the benefits of flexible working arrangements and strives to provide a balance that works for both employees and the organization.

In your answer, focus on specific projects where you've implemented Hadoop, detailing the scale of data processed and the outcomes. Highlight your familiarity with its ecosystem, including tools like Pig, Hive, or Spark, and how it fits into your overall data engineering approach.

Discuss your strategies for maintaining data quality, such as implementing validation checks, routine audits, and leveraging ETL tools to cleanse and standardize data from disparate sources for better accuracy and consistency.

Articulate your approach to performance testing by mentioning specific metrics you track, tools you use, and how you analyze performance bottlenecks. Provide an example of how you've successfully optimized a data pipeline.

Share how you keep up with industry trends through professional development resources, online courses, or attending conferences. Mention any relevant certifications, and discuss key learnings that you’ve integrated into your work.

Use the STAR (Situation, Task, Action, Result) method to highlight a particularly challenging project. Describe the problem, your approach to solving it, any tools or methodologies you applied, and the successful outcome of your efforts.

Discuss your understanding of Data Warehousing principles such as ETL processes, dimensional modeling, and the role of storage systems. Provide specific examples of how you've been involved in data warehousing projects.

Emphasize your ability to work collaboratively, mentioning tools or practices you employ to facilitate communication and teamwork. Provide examples of how you’ve successfully worked across teams on complex data projects.

Discuss your organizational skills, the methods you use to prioritize tasks, and how you balance both short-term and long-term project goals. Highlight any frameworks you follow, like Agile or Kanban.

Mention popular tools like SQL, Python, or specific data manipulation libraries. Explain why you favor these tools, considering factors such as usability, efficiency, or compatibility with existing systems.

Describe your debugging process, including techniques for identifying issues, tools you rely on for troubleshooting errors, and your method for documenting and learning from the debugging process for future reference.