Data Engineer - Sr. Consultant level- Core Data Assets - job 17 of 21

As a Data Engineer - Sr. Consultant level at Visa, your main responsibilities include designing, coding, and testing data models, creating data pipelines, and ensuring data quality through validation and enrichment. You'll also implement necessary services for AI and data science models, while designing fine-tuned data models that meet our requirements. Additionally, providing technical leadership and developing partnerships with product delivery teams to implement proofs of concept will be key aspects of your role.

To be considered for the Data Engineer - Sr. Consultant position at Visa, candidates typically should hold a relevant degree in computer science, data engineering, or a related field, along with substantial experience in data architecture or engineering. Proficiency in programming languages, data modeling, and AI solutions are essential. Prior experience with big data processing tools and strong analytical skills will also be crucial for success in this role.

Visa actively supports innovation in the Data Engineer - Sr. Consultant role by encouraging contributions to projects that enhance data-driven business services. You’ll have opportunities to lead efforts around tooling and infrastructure that drive agile development, and you'll be expected to present your innovative ideas in technology talks, leveraging your expertise to influence the direction of data engineering within the organization and across the industry.

In the Data Engineer - Sr. Consultant role at Visa, you'll engage with a wide range of technologies, including data processing platforms, AI and machine learning tools, and database management systems. Familiarity with programming languages such as Python, Scala, or Java, along with experience in big data technologies like Hadoop and Spark, will be highly valuable as you design and manage robust data pipelines and architectures.

The work environment for a Data Engineer - Sr. Consultant at Visa is dynamic and collaborative. This hybrid role allows you to alternate between working remotely and in the office, promoting a flexible lifestyle while ensuring teamwork and communication. You'll be part of a passionate team dedicated to advancing data-related initiatives within a fast-paced industry, allowing for continuous learning and growth.

When answering this question, focus on specific examples from your previous roles where you designed and implemented data pipelines. Discuss the tools and techniques you used, such as ETL processes, data validation methods, and any challenges you overcame during the development. Highlight the impact of your work on project outcomes and how it enhanced data accessibility and quality.

Talk about your systematic approach to maintaining data quality, which may include methods like data validation, cleansing, and enrichment. Share specific tools and processes you've utilized to monitor data accuracy and integrity throughout the pipeline, and provide examples of times when you identified and resolved data issues.

Explain your commitment to professional development by mentioning any relevant courses, certifications, or industry conferences you actively participate in. Additionally, discuss how you engage with data engineering communities or follow influential thought leaders to stay abreast of innovations and best practices in the field.

Frame your response around a specific project that posed significant challenges. Detail the project's goals, your approach, the technologies used, and the outcomes. Highlight any skills you utilized to overcome obstacles and what you learned that could benefit your role at Visa.

Share your philosophy on collaboration, emphasizing the importance of communication, feedback, and shared goals. Provide examples of successful teamwork in previous projects, focusing on how you fostered a productive environment, managed conflicts, and integrated diverse perspectives to achieve project success.

Explain your foundational knowledge of AI and machine learning concepts and how they relate to data engineering. Discuss how data engineers support AI model deployment and management, which could include data preparation, feature engineering, and model monitoring, highlighting examples of how you've worked with these technologies.

Walk the interviewer through your previous design experiences, focusing on the types of data models you've created, such as conceptual, logical, and physical models. Discuss frameworks, tools, or methodologies you used in the design process and how your models helped meet business requirements effectively.

Demonstrate your problem-solving skills by outlining your approach to identifying and resolving data quality issues in production. Discuss the steps you would take to investigate the root cause, communicate with stakeholders, and implement corrective measures, all while minimizing disruptions to business operations.

Highlight the importance of data governance in ensuring that data practices align with regulatory requirements and best practices. Affirm your understanding of data privacy, security protocols, and how you incorporate governance principles into your data engineering processes to manage risk and ensure compliance.

Craft a response that aligns your career aspirations with Visa's mission and values. Discuss your passion for innovation in the data space, your interest in the digital payments landscape, and how your unique skills and experiences can contribute to Visa's ongoing success in leveraging data to enhance customer experiences and drive business growth.