Data Engineer - Sr. Consultant level - job 14 of 19

As a Data Engineer - Sr. Consultant at Visa Technology & Operations, your primary responsibilities include designing and enhancing fraud detection solutions, collaborating with product stakeholders to translate business challenges into technical data problems, leading end-to-end development efforts, and mentoring junior team members. You will also be involved in creating and maintaining scalable data engineering processes and participate in cross-team functions, addressing various project needs.

To qualify for the Data Engineer - Sr. Consultant position at Visa Technology & Operations, candidates typically need a strong background in data engineering, proficiency with programming languages such as Python or Java, and experience with SQL databases. A solid understanding of data frameworks and architecture is essential, along with the ability to work in a collaborative environment. Advanced degrees in Computer Science, Data Engineering, or related fields may be preferred.

At Visa Technology & Operations, a Data Engineer - Sr. Consultant will engage in a range of projects, particularly focusing on fraud detection and the enhancement of data-driven business solutions. The projects will vary in scope from small analyses to large-scale implementations involving collaboration with various teams to deliver robust data solutions that operate efficiently under predefined Service Level Agreements.

Mentorship is a valuable aspect of the Data Engineer - Sr. Consultant's role at Visa Technology & Operations. Experienced professionals are encouraged to guide and support junior team members, sharing knowledge about best practices in data engineering and promoting skills development within the team. This mentorship not only enriches the team’s capabilities but also fosters a collaborative environment.

As a Data Engineer - Sr. Consultant at Visa Technology & Operations, you will have numerous opportunities for career growth. With a focus on innovation and technology, you can advance into higher leadership roles within data engineering, transition to product management, or take on specialized positions that align with your interests in data analytics or architecture.

When discussing your experience with data engineering projects, focus on specific examples where you have successfully managed project timelines, collaborated with different teams, and demonstrated technical expertise. Highlight challenges faced during these projects and how you overcame them to deliver quality solutions.

In ensuring data quality and integrity, outline methods such as implementing validation checks, regular audits, and monitoring processes. Discuss how your proactive approach to problem-solving has helped maintain high standards in previous roles.

Discuss the array of technologies and tools you are comfortable with, such as SQL databases, ETL tools, data warehousing technologies, and programming languages like Python or Java. Emphasize your adaptability to new technologies as data engineering continues to evolve.

When sharing a challenging data problem, provide a concise overview of the scenario, the approach you took to analyze the issue, and the final solution implemented. Highlight your analytical skills and creative thinking during this process.

Elaborate on your collaboration methods, emphasizing clear communication, alignment of project goals, and regular check-ins to ensure everyone is on the same page. Share a specific experience where teamwork led to a successful outcome.

Share your mentoring philosophy, focusing on constructive feedback, providing resources for learning, and encouraging open communication. Illustrate a successful mentoring experience where a junior member thrived under your guidance.

Discuss your time management and organizational skills, emphasizing techniques like prioritization frameworks and project management tools that help you stay on track. Provide an example of how you successfully navigated competing deadlines in the past.

Provide details of a data architecture you designed, including the rationale behind your decisions and the tools used. Explain how it addressed the specific needs of the business and the impact it had on data retrieval and analysis.

Discuss emerging trends in data engineering, such as advancements in AI and machine learning, cloud computing, and the increasing importance of real-time analytics. Share how excited you are to partake in these developments and your readiness to adapt.

Talk about your open-minded approach to feedback, emphasizing its significance for personal and professional growth. Provide a scenario where constructive criticism led to a positive outcome in your work.