Data Engineer - Sr. Consultant level - job 10 of 19

As a Data Engineer - Sr. Consultant level at Visa Technology & Operations LLC, you will be responsible for designing and developing advanced fraud detection solutions, transforming business problems into technical data challenges, and collaborating with cross-functional teams to create innovative data-driven solutions. You'll lead end-to-end development efforts, mentor junior team members, and ensure that projects are delivered on time while adhering to quality standards.

To qualify for the Data Engineer - Sr. Consultant level position at Visa Technology & Operations LLC, candidates should possess a strong background in data engineering, software development skills, and experience with agile methodologies. A degree in Computer Science or a related field, along with a proven track record of delivering complex data projects, is essential. Familiarity with data governance and analytics tools will give candidates an edge.

At Visa Technology & Operations LLC, a Data Engineer - Sr. Consultant level can expect ample opportunities for professional development and growth. You’ll be mentoring junior engineers, engaging in diverse projects, and being part of a collaborative environment that encourages learning and skill enhancement. This role also exposes you to advanced technologies and best practices in the data engineering field, paving the way for your career advancement.

The work environment for a Data Engineer - Sr. Consultant level at Visa Technology & Operations LLC is dynamic, collaborative, and geared towards innovation. You’ll find yourself within a supportive team where ideas are freely exchanged, and you’ll be working on exciting projects that aim to enhance fraud detection capabilities. Additionally, the flexibility of partial telecommuting allows you to achieve a good work-life balance.

A Data Engineer - Sr. Consultant level at Visa Technology & Operations LLC will work on projects ranging from developing fraud detection solutions to implementing self-service data tools for data scientists. You'll be involved in analytics tool evaluations, data governance, and scaling automated analyses, ensuring that your contributions have a significant impact on the company's data infrastructure and overall success.

When answering this question, highlight your proficiency with relevant data engineering tools such as Apache Spark, Hadoop, or any ETL solutions you’ve used. Mention specific projects where you applied these technologies and the outcomes, emphasizing your technical skills and understanding of the data engineering process.

In your response, explain your problem-solving methodology. Discuss identifying the core issue, breaking it down into manageable parts, and using data analysis techniques to develop solutions. Provide an example of a challenging data problem you've solved, and explain the steps you took to arrive at a resolution.

Talk about the importance of data quality and share your strategies such as implementing validation checks, data profiling, and regular audits. Provide instances where your measures led to improved data accuracy, which can demonstrate your commitment to maintaining high standards in data engineering.

Emphasize your organizational skills and prioritization strategies. Illustrate with an example of a time when you successfully balanced multiple projects, highlighting how you managed your time and communicated with stakeholders to ensure timely delivery without sacrificing quality.

Collaboration is crucial in data engineering, especially when working with cross-functional teams. Mention how you value inputs from product managers, analysts, and others in developing solutions. Share a specific experience where strong collaboration resulted in a successful project outcome to strengthen your point.

Provide an example where your data contributions led to a significant decision being made or an improvement in processes. Explain how you analyzed the data and communicated your findings effectively, showcasing the impact of data engineering on business outcomes.

Discuss your approach to building scalable data solutions such as modular design, cloud infrastructure, and efficient coding practices. Provide a real-world example that illustrates your ability to design systems that can grow with the demands of the business.

Mention your commitment to continuous learning through attending workshops, following industry experts online, and engaging with communities related to data engineering. You can also point out specific resources you find useful in keeping up-to-date with the evolving landscape of data technologies.

When addressing this question, emphasize your communication skills. Describe how you simplify complex concepts using analogies or visual aids, ensuring that your audience grasps the key points. Share an example where you effectively communicated technical details to a non-technical audience.

Discuss the key factors you consider, such as scalability, data integrity, performance, and future growth. Offer an example of a data architecture project you’ve worked on and how you ensured it met these considerations while fulfilling business needs.