Data Engineer - job 1 of 7

As a Data Engineer at Visa in Bangalore, you will be responsible for developing and maintaining test automation scripts using PySpark, collaborating with data engineers to understand workflows, and designing automated tests for data ingestion and processing in a Hadoop ecosystem. You'll also perform data validation, manage CI/CD pipelines, optimize Spark jobs, and ensure compliance with business requirements through thorough QA testing.

To qualify for the Data Engineer position at Visa, you should possess a bachelor's degree in computer science, Information Technology, or a related field, or have a minimum of 3 years of relevant work experience. Relevant certifications in Big Data or Spark would be advantageous, alongside a solid understanding of ETL processes and tools.

The work environment for a Data Engineer at Visa in Bangalore is collaborative and fast-paced. You’ll be working in a hybrid model, allowing some flexibility between remote work and in-office collaboration. The culture fosters teamwork and values each member’s contribution to the overall success of projects, encouraging open communication and innovation.

Yes, the Data Engineer position at Visa offers a hybrid work model, which means you can work part of the time remotely. The specific expectations regarding in-office days will be confirmed by your hiring manager, providing you with a balance between remote flexibility and in-person collaboration.

At Visa, professional development is a priority. As a Data Engineer, you will have access to various training programs, workshops, and resources to enhance your skills in emerging technologies like Big Data and Spark. This commitment to continuous learning ensures you stay at the forefront of the industry and can grow your career meaningfully.

When answering this question, focus on specific examples where you've utilized PySpark for data processing. Describe how you developed automation scripts and what challenges you faced, showcasing your problem-solving skills and familiarity with Spark concepts like RDDs and DataFrames.

Discuss techniques such as partitioning, caching, and choosing the right data structures. Provide instances where your optimizations led to measurable performance improvements, and explain your thought process when approaching job tuning.

Use the STAR method (Situation, Task, Action, Result) to outline a specific situation where you identified a defect. Discuss your communication methods with developers and how your collaborative efforts led to a positive outcome.

Explain your approach to data validation and verification processes. Provide insights into specific testing frameworks or methodologies you use to maintain data quality within big data applications.

Emphasize the importance of Continuous Integration and Continuous Deployment in streamlining workflows. Share your experience in setting up CI/CD pipelines and how they enhance testing frequency and reliability.

Discuss your strategies for effective communication and collaboration with stakeholders in different regions. Highlight the importance of flexibility and scheduling tools in facilitating seamless interactions.

Describe your familiarity with Hadoop and related tools. Provide examples of how you've integrated Hadoop into your testing frameworks and how it aligns with data processing requirements.

Share your methods for professional development, such as online courses, attending industry conferences, and participating in community forums. Highlight the value of continuous learning in the fast-evolving tech landscape.

Detail your QA processes, including requirement analysis, test case development, and execution. Discuss specific payment standards or regulations you adhere to, ensuring compliance and quality.

Emphasize the importance of clear and comprehensive documentation. Share tools you use for documentation, along with your method for ensuring that records are easily understood and helpful for future reference.