Data Engineer - Sr. Consultant level- Core Data Assets - job 6 of 21

As a Data Engineer - Sr. Consultant at Visa, you will handle a range of responsibilities including designing, coding, and testing data models by building effective data pipelines. You'll ensure data quality by implementing thorough validation and cleansing processes. Additionally, this role focuses on leading the development of AI and data science models that can process terabytes of information. You will also collaborate with various teams to implement innovative solutions that meet Visa's data strategy.

To qualify for the Data Engineer - Sr. Consultant position at Visa, candidates typically need a strong background in data engineering, including experience with data modeling, building data pipelines, and a solid understanding of AI and data science principles. Proficiency in programming languages such as Python, familiarity with data processing frameworks, and effective communication skills are essential for success in this role.

The work environment for a Data Engineer - Sr. Consultant at Visa is dynamic and collaborative, providing opportunities to work both independently and part of a team. This hybrid position allows professionals to alternate between remote work and being in the Foster City office. Employees are expected to be in the office for 2-3 set days weekly, fostering teamwork and innovation within the organization.

Data Engineers at Visa work with cutting-edge technologies and tools that support large-scale data processing and AI model deployment. Common technologies include data warehousing solutions, data processing frameworks like Apache Spark, and various tools for data validation, cleansing, and enrichment. Additionally, familiarity with machine learning libraries and cloud services is often beneficial for this role.

The Data Engineer - Sr. Consultant role is integral to Visa's business strategy as it directly impacts the quality and availability of data that drives innovation. By developing robust data pipelines, enriching data, and implementing AI solutions, this position helps refine product offerings, enhance customer experiences, and ultimately maintain Visa's competitive edge in the digital payment landscape.

When discussing your experience with data pipeline development, focus on specific projects where you designed and implemented pipelines. Highlight the tools you utilized, such as Apache Spark or Azure Data Factory, and discuss any challenges you faced in ensuring data quality and performance. Sharing metrics or outcomes achieved through your pipelines can showcase the impact of your work.

In your response, detail the methods you use for data validation, such as automated testing or checksums. Explain how you address data quality issues, including the processes you employ for cleansing and enrichment. Providing examples of past scenarios where you successfully improved data integrity will strengthen your answer.

Share specific instances when you have worked with machine learning models, from the development phase to deployment. Discuss the tools you utilized, such as TensorFlow or Scikit-learn, and your role in integrating these models into operational workflows. Highlight experiences with continuous monitoring and optimizing model performance post-deployment.

Discuss your project management skills, emphasizing tools or strategies you utilize for effective task prioritization. You might mention methodologies such as Agile or Kanban, and the importance of communication with team members to align on project timelines and milestones.

Be prepared to describe a specific complex data problem, the approach you took, and the solution you implemented. Discuss the tools and technologies involved, the stakeholders affected, and the results of your solution. This will demonstrate your analytical thinking and problem-solving skills.

Discuss your familiarity with cloud platforms like AWS, Google Cloud, or Azure, and highlight specific services you've used related to data engineering, such as data lakes or managed databases. Mention any certifications you hold and how these technologies have played a role in your engineering projects.

Explain your methods for keeping up with the industry, such as following influential thought leaders, attending webinars, or participating in online courses. Sharing specific examples of how you've applied this knowledge in your work will make your answer more impactful.

Talk about the techniques you use to simplify complex technical concepts, such as using analogies or focusing on the business impacts rather than the technical details. Providing an example where you've successfully communicated with non-technical stakeholders will enhance your credibility.

Describe a particular data architecture you've worked on, focusing on the key components such as data sources, storage solutions, and data processing frameworks. Illustrate how you ensured scalability and adaptability in your design to support evolving business needs.

Clarify your understanding of data governance principles, including data quality, accountability, and regulatory compliance. Discuss any frameworks you're familiar with and how applying these principles has impacted your data engineering projects in previous roles.