Data Engineer - job 2 of 7

As a Data Engineer at Visa in Bangalore, your primary responsibilities will include designing, developing, and maintaining large-scale data applications tailored for enterprise use. You'll be creating and managing data pipelines, ensuring efficient ETL processes while leveraging tools like Apache Spark and Hadoop. Additionally, you'll be involved in productizing AI/ML models, contributing to transformative data solutions that drive Visa’s mission.

To qualify for the Data Engineer position at Visa, candidates typically need a Master's or Bachelor's degree in computer science or a related field. A minimum of 3 years of relevant work experience is highly preferred, alongside expertise in big data technologies such as Hadoop, Apache Spark, and proficiency with SQL. Familiarity with data storage technologies and the ability to develop effective ETL processes are also crucial.

A Data Engineer at Visa should be proficient in big data technologies including Hadoop and Apache Spark. Additionally, strong skills in Python and SQL are essential. Familiarity with data management tools like Kafka, Redis, and Flink, as well as experience with data storage solutions like MySQL or MS SQL, will also be beneficial in this role.

The work environment for Data Engineers at Visa in Bangalore is collaborative and innovative, encouraging knowledge sharing among team members. As part of a hybrid work model, you’ll have flexibility in where you work, while still enjoying in-office collaboration. Visa values a work culture that promotes networking and personal growth, allowing you to thrive in your role.

Visa is deeply committed to the professional development of its employees, including Data Engineers. Opportunities for continuous learning through workshops, online courses, and mentorship programs are readily available. This focus on growth ensures that you can stay updated with the latest technologies and best practices in the field of data engineering.

When answering this question, detail your hands-on experience with Hadoop and Spark. Discuss specific projects where you utilized these frameworks, the challenges you faced, and how you overcame them. Employers appreciate candidates who can demonstrate how they’ve applied their knowledge practically.

To effectively answer this question, outline the steps you take to maintain data quality, such as implementing validation checks, logging errors, and monitoring data flows. Providing examples of how you’ve handled discrepancies will illustrate your proactive approach to ensuring data accuracy.

When discussing your SQL skills, mention specific databases you've worked with, the complexity of queries you've written, and how you’ve optimized performance. Emphasize your understanding of database design and administration to show your comprehensive grasp of database management.

Mention your familiarity with tools like Airflow and Control-M, highlighting how you’ve used these tools for scheduling and managing data workflows. Provide examples of tasks you automated, improving efficiency, and how this had an impact on project outcomes.

Here, describe your troubleshooting methodology—starting from root-cause analysis, checking data logs, and identifying bottlenecks. An example from your past experience where you successfully resolved a critical issue will reinforce your adeptness in handling such challenges.

In answering this question, choose a specific project that required you to interact with different departments, such as product management or data science teams. Discuss your role, the collaboration tools you used, and how this teamwork contributed to the project’s success.

Detail your proficiency in Python, mentioning libraries or frameworks you’ve utilized for data manipulation or analysis. Discuss any specific scripts you’ve created for ETL processes or data pipelines and how they improved the overall efficiency of data handling.

When addressing this question, discuss strategies such as partitioning data, indexing, or using caching techniques. Providing concrete examples on how you’ve implemented these strategies to enhance query performance showcases your expertise.

In your response, explain your familiarity with deploying machine learning models into production. Discuss the frameworks you’ve used and the lifecycle management of these models, highlighting how you ensured they performed as expected post-deployment.

Discuss your commitment to continuous learning through resources like online courses, industry conferences, and networking with other professionals in the field. Mention blogs, podcasts, or social media channels you follow to keep abreast of new tools and methodologies in data engineering.