Staff Data Engineer-Scala/Big Data-6+yrs

As a Staff Data Engineer at Visa, you'll be responsible for designing, architecting, and coding large-scale Data as a Service (DaaS) solutions. You'll work collaboratively with stakeholders and product owners to create software components that are multi-tenant and high-performance. Your role also involves building data-driven business solutions and solving complex data problems using advanced technologies like Hadoop and Spark.

To qualify for the Staff Data Engineer position at Visa, you should have at least 6 years of relevant work experience, preferably with a Bachelor's or Master's degree in Computer Science or a related field. Additionally, experience with Big Data technologies, programming languages like Java or Scala, and proficiency in building scalable data solutions are essential for this role.

Preferred skills for the Staff Data Engineer position at Visa include extensive experience with Big Data technologies such as Hadoop, Spark, and Kafka, as well as strong design and coding skills. Familiarity with SQL and NoSQL databases, as well as experience with Continuous Integration tools like Jenkins, is also beneficial.

Yes, experience with cloud technologies is highly valued for the Staff Data Engineer position at Visa. Knowledge of containerization tools like Docker and orchestration platforms such as Kubernetes will give you a competitive edge, especially when designing and deploying scalable applications in a cloud environment.

At Visa, we foster a collaborative and inclusive work environment where innovative thinking is encouraged. As a Staff Data Engineer, you'll work closely with cross-functional teams and have the opportunity to contribute to meaningful projects that impact the global payments landscape. We believe in empowering our employees and celebrating diversity, creating a culture that is as unique as our mission.

When answering this question, detail your experience by outlining the key components of the architecture you developed. Highlight technologies like Hadoop, Spark, and any data storage solutions you implemented, noting how they contribute to scalability and performance.

Discuss your approach to maintaining data quality, including strategies for validation, testing, and monitoring within your data pipelines. Mention tools and frameworks you’ve used to automate these processes to ensure reliability.

Explain your methodology for optimizing SQL queries, including indexing, partitioning, and using query execution plans to identify bottlenecks. Providing a specific example from your previous experience can illustrate your approach effectively.

Describe your process for managing schema evolution in data storage solutions, including communication with stakeholders and the strategies you implement to minimize disruptions. Discuss tools like Apache Hive or SQL interfaces you might have utilized.

Detail any projects where you've implemented Apache Kafka for event streaming. Discuss how you set up producers and consumers, managed topics, and ensured data consistency and reliability within your systems.

Share your experience in designing and implementing REST APIs, emphasizing best practices such as statelessness, consistent naming conventions, and error handling. Highlight any frameworks you've used, like Spring Boot or Express.js.

Illustrate your experience with collaboration by mentioning a specific project. Explain how you engaged with product owners to understand requirements, define project scopes, and iterate on feedback during the development process.

Discuss specific challenges you encountered during past projects, such as data ingestion speed or system latency. Describe how you identified these issues and the strategies you employed to address them effectively.

Provide an overview of your experience with Docker for containerization and Kubernetes for orchestration. Explain how they've improved your development workflow and deployment strategies in your previous roles.

Define your approach to continuous integration and deployment, mentioning tools like Jenkins or GitLab. Discuss how you automate testing and deployment processes to ensure high-quality releases.