Sr. Data Engineer- Java/Big Data

As a Sr. Data Engineer at Visa, your primary responsibilities include overseeing the design and implementation of big data solutions, enhancing AI platforms, and managing Visa's technology assets within the Data Platform department. You'll collaborate on building scalable data pipelines, ensuring data availability for analytics, and optimizing existing processes. Your expertise in big data technologies and software development will play a crucial role in innovating Visa's services.

To qualify for the Sr. Data Engineer role at Visa, you should either hold a Bachelor's degree or have a minimum of 2+ years of relevant work experience. Proficiency in programming languages like Java, Scala, or Python is essential, along with experience in engineering practices and writing high-quality code. Familiarity with building platforms on big data open source technologies like Hadoop, Hive, or Spark will further strengthen your application.

Candidates applying for the Sr. Data Engineer position at Visa should be well-versed in big data technologies. Familiarity with platforms such as Hadoop, Hive, Spark, and Tez is crucial. Additionally, contributions to big data open source technologies like Kafka or HBase will be considered a significant advantage. Your command over these technologies will help in building effective data solutions.

The Sr. Data Engineer position at Visa follows a hybrid work model. Employees are expected to work from the office 2-3 set days a week, with the flexibility to work remotely the remaining days. This approach emphasizes collaboration while offering the convenience of remote work, allowing you to find a balance that fits your lifestyle and needs.

Visa is committed to the professional development of its employees, and the Sr. Data Engineer position offers significant career growth potential. Opportunities exist to lead projects, mentor junior engineers, and expand your technical knowledge. As you contribute to innovative projects and enhance your skill set, you may also explore roles of increased responsibility within the company.

This question allows you to showcase your hands-on experience with Hadoop. Discuss specific projects where you've implemented Hadoop, detailing the challenges you faced and how you overcame them. Highlight the results of your work and the impact it had on the organization.

To ensure code quality, I follow best practices such as writing unit tests, conducting peer code reviews, and adhering to coding standards. I also utilize CI/CD pipelines to automate testing and deployment processes, ensuring that any code pushed to production is stable and performant. Sharing examples of your practices would enhance your answer.

When addressing challenging data problems, focus on your problem-solving strategies. Outline the problem, your approach to devising a solution, and the end results. Emphasize analytical thinking and possibly teamwork if relevant. This showcases your capability to handle complexities in data engineering.

In your answer, describe specific AI projects you've worked on that leveraged big data technologies. Discuss how data engineering facilitated data preparation for AI models and any specific techniques or tools you used. Showcasing a successful integration would further strengthen your answer.

Managing data versioning and lineage is critical in large data projects. Discuss the tools and methodologies you've used, such as Apache Atlas or Data Catalogs, to track data flow and changes. Emphasize how this practice ensures compliance, quality, and ease of collaboration among teams.

Discuss the big data tools and frameworks you are most experienced with, such as Spark, Hive, or Kafka. Provide context on how you have used each tool in past projects and your comfort level, emphasizing any innovative solutions you implemented using these technologies.

Explain your criteria for selecting Spark over MapReduce by discussing the specific advantages of Spark, such as speed and ease of use. Share instances where you've made this choice in projects, outlining the performance differences and outcomes achieved.

It's critical to prioritize data security and privacy. Discuss best practices you follow, such as data encryption, access controls, and compliance with laws like GDPR. Sharing experiences where you've implemented these practices could reinforce your commitment to data stewardship.

Optimization of data pipelines includes various strategies like reducing data ingestion time and improving processing speeds. Discuss specific techniques you've implemented, such as utilizing partitioning in databases or tweaking job parameters to optimize resource usage. Provide examples where applicable.

Continuous learning is essential in technology. Discuss how you subscribe to relevant online forums, attend industry conferences, or participate in online courses. Mention any recent advancements in big data that excite you and how you envision applying them in your work.