Data Engineer - job 3 of 7

As a Data Engineer at Visa in Bangalore, your primary responsibilities include designing and deploying data pipelines, building data warehouses and models, and supporting Business Intelligence solutions. You will work with data in both structured and unstructured forms and collaborate with Data Analysts and Data Scientists. The role also involves utilizing PySpark and SQL for data manipulation, while developing reports and visualizations using Tableau and Power BI to extract meaningful insights.

To become a Data Engineer at Visa, candidates typically need a Bachelor's degree in engineering, Analytics, Data Science, Computer Science, Statistics, or Math. Additionally, at least 3 years of relevant work experience is preferred. Hands-on experience with big data technologies like Hadoop, Spark, and database systems like SQL is crucial, along with a solid understanding of data modeling, ETL processes, and BI tools like Tableau and Power BI.

In your role as a Data Engineer at Visa, you will work with a variety of technologies including PySpark, SQL, and various big data frameworks like Hadoop and Spark. You will also engage with Business Intelligence tools such as Tableau and Power BI for data visualization and reporting. Familiarity with machine learning frameworks and languages such as Python and R will also be beneficial for implementing predictive models and advanced analytics.

The work culture for Data Engineers at Visa is collaborative, innovative, and supportive. With a strong emphasis on teamwork, you’ll work alongside Data Analysts, Data Scientists, and other experts in cross-functional teams. Visa promotes high standards of accountability and ownership, valuing creative solutions and customer-centric approaches. The flexible hybrid work model allows for a balance between in-office collaboration and remote work.

Visa supports professional development by encouraging continuous learning and skill enhancement. Data Engineers are provided with opportunities to work on cutting-edge technologies, attend workshops, and participate in training programs specific to data engineering, machine learning, and analytics. Regular performance evaluations and mentorship programs also ensure that employees can set and achieve their career growth goals.

In answering this question, focus on your relevant projects where you designed and built data pipelines. Highlight the technologies you used, such as PySpark or SQL, and explain how you ensured the efficiency and reliability of these pipelines. Discuss any challenges you faced and how you overcame them, emphasizing your problem-solving skills.

Concisely explain what ETL stands for: Extract, Transform, Load. Discuss your experience with ETL processes, including the tools you've used and your role in the design and implementation. Provide examples of how you've optimized ETL workflows for better performance and data accuracy.

Emphasize the importance of data quality in your response. Discuss methodologies you've used for validating and cleaning data, such as data profiling techniques or automated tests that ensure integrity. Provide examples of how you've implemented data quality checks in previous projects.

Detail your hands-on experience with visualizations using Tableau or Power BI. Talk about the types of dashboards you've created and how they have supported decision-making processes. Mention any specific features or best practices you follow to enhance the usability of these reports.

Choose a project that demonstrates both your technical skills and your ability to drive results. Describe the project’s scope, your role, and the outcomes achieved. Highlight your collaborative efforts with cross-functional teams and any technologies utilized.

Talk about any experience you've had in developing machine learning models, including the algorithms used and how you applied these models to real-world data problems. Discuss your familiarity with frameworks like TensorFlow or Scikit-learn, if applicable.

Discuss your experience with version control systems, particularly Git. Explain how you use branching, merging, and pull requests to manage code changes effectively and prevent conflicts in collaborative environments.

Highlight specific tactics you've implemented for SQL performance tuning, such as indexing strategies, query optimization techniques, or examining execution plans. Illustrate your responses with examples from past experiences.

Describe OLAP (Online Analytical Processing) and its role in business intelligence. Explain how OLAP helps in efficient data analysis by allowing users to perform multidimensional analysis and generate reports quickly. Mention any experience you have with OLAP tools or systems.

Highlight skills such as programming proficiency in PySpark or SQL, strong analytical abilities, teamwork, effective communication, and problem-solving skills. Emphasize the need for adaptability in this fast-evolving field and your commitment to continuous learning.