Data Engineer - Sr. Consultant level- Core Data Assets - job 7 of 21

As a Data Engineer - Sr. Consultant at Visa, you will be responsible for designing, coding, and testing data models, mining and building data pipelines, and ensuring data quality through validation processes. Your role also involves implementing AI and data science model services, developing conceptual and physical data models, and providing technical leadership to enhance data engineering projects. You'll work closely with product delivery teams to implement innovative solutions that enhance Visa’s offerings.

To excel as a Data Engineer - Sr. Consultant at Visa, candidates should possess a strong background in data engineering, experience with large-scale data processing, and familiarity with AI and machine learning concepts. A solid understanding of data modeling techniques and relevant technology stacks is essential. Additionally, strong communication and collaboration skills are critical for effectively translating technical solutions into business terms.

Essential skills for success as a Data Engineer - Sr. Consultant at Visa include expertise in data pipeline construction, proficiency in data validation and cleansing techniques, and the ability to implement AI model solutions. Familiarity with databases, data engineering tools, and a capacity for delivering technical solutions in a business context are also important. An innovative mindset geared towards collaboration and efficiency will further enhance your effectiveness in this role.

At Visa, data engineers are encouraged to champion innovation across the organization and the broader industry. The Data and Artificial Intelligence Platform group provides unique opportunities for continuous learning and development through consulting and technology talks. By collaborating on impactful projects and contributing to the advancement of data science and AI-driven solutions, Data Engineers can significantly influence their career growth and Visa’s mission in the digital payments landscape.

The work environment for a Data Engineer - Sr. Consultant at Visa is a hybrid setting that allows flexibility between remote and office work. Employees are expected to work from the office 2-3 days a week, fostering collaboration and teamwork, while also enjoying the freedom that remote work offers. This arrangement accommodates diverse working styles and enhances the ability to innovate and drive projects forward in a supportive environment.

When answering this question, focus on specific examples where you built data pipelines from scratch. Highlight your approach to data extraction, transformation, and loading (ETL) processes, the tools you used, and how you ensured data quality and integrity throughout. Emphasizing collaboration with teams in your projects can also showcase your teamwork skills.

Discuss the various strategies you implement for data quality assurance, such as data validation checks, cleansing processes, and regular audits. Include any specific tools or methodologies (e.g., Agile, Data Governance frameworks) that you have used to maintain high standards of data integrity, and share an example of how you applied these strategies in a previous project.

When answering this, explain your step-by-step approach to designing conceptual, logical, and physical data models. Discuss how you gather requirements, consider user needs, and evaluate data relationships. Mention tools you have utilized (e.g., ER diagrams, UML) and any experience in database normalization and performance optimization.

Choose a relevant challenge you encountered in a past project, detailing the issue and your thought process in addressing it. Focus on the methods you implemented to solve the problem, the lessons learned, and how it improved your overall workflow. This shows your problem-solving skills and resilience in the data engineering field.

Discuss your experience in working with cross-functional teams, particularly product delivery teams, emphasizing clear communication, understanding stakeholder needs, and contributing to project objectives. Provide examples of how your contributions facilitated successful project outcomes and strengthened collaboration.

Describe your specific experiences with integrating AI and machine learning into your data engineering efforts. Talk about any frameworks or libraries you’ve used, how you’ve implemented predictive models, and the impact of your work on business outcomes. Highlight your willingness to learn and adapt as AI technology evolves.

Detail the techniques and tools you use for monitoring data pipelines, including logging, alerts for failures, and performance metrics. Share an example where proactive maintenance or monitoring prevented significant issues and highlight any automation techniques you applied to enhance efficiency.

List the programming languages (e.g., Python, SQL, Scala) and tools (e.g., Apache Spark, Kafka, Airflow) that you are skilled in. Discuss your depth of experience with each, providing examples of projects where you successfully applied these technologies to solve data challenges.

Articulate your philosophy on teamwork, emphasizing the importance of communication, respect for diverse perspectives, and shared goals. Provide anecdotes that showcase your collaborative skills, detailing how you’ve worked with various team members to drive projects towards successful completion.

This is a great opportunity to convey your passion for data engineering and how Visa’s mission resonates with your personal and professional values. Discuss what excites you about the role, how it aligns with your skill set, and why you believe Visa is the perfect environment for you to innovate and contribute to impactful projects.