Staff Data Engineer - job 3 of 50

As a Staff Data Engineer at Visa, you’ll be responsible for designing and developing data engineering pipelines, collaborating with business stakeholders, and creating analytic data assets. Your role will also involve building and maintaining a self-serve data environment, automating analyses, and mentoring junior engineers. You will actively contribute to optimizing existing solutions and developing pipelines that support Gen AI projects.

To qualify for the Staff Data Engineer position at Visa, candidates should have at least 5 years of relevant work experience or a combination of education and experience that meets the company's requirements. A solid background in Big Data technologies such as Hadoop and Spark, proficiency in programming languages like Python or Java, and familiarity with cloud platforms are preferred. Additionally, strong problem-solving skills and experience with ETL tools are important.

The Staff Data Engineer at Visa plays a crucial role in harnessing data to drive insights and decisions that align with the company’s mission to connect the world through innovative payment solutions. By building effective data pipelines and self-serve tools, you will empower various teams to leverage data for better outcomes, thus enhancing Visa's ability to operate efficiently and support customers worldwide.

For the Staff Data Engineer role at Visa, candidates should have strong programming skills in languages such as Python, Java, or Scala. Mastery of Pyspark is crucial, along with an understanding of object-oriented programming concepts. Experience with Big Data technologies and knowledge of ETL tools will also enhance your effectiveness in this position.

As a Staff Data Engineer at Visa, you can expect a hybrid work environment that encourages collaboration and innovation. The position allows for flexibility in choosing your work location, typically combining in-office days with remote work. This dynamic setup not only promotes a healthy work-life balance but also fosters a culture of teamwork and shared goals.

When answering this question, discuss specific projects where you've used Big Data tools like Hadoop or Spark. Highlight your contributions, such as building data pipelines or developing analytical solutions, to showcase your hands-on experience and technical expertise.

Use the STAR method (Situation, Task, Action, Result) to frame your answer. Describe the project’s context, the challenges you faced, your approach to resolving those issues, and the positive outcomes that resulted from your actions.

Explain your methods for maintaining code quality, such as reviewing code with peers, implementing automated testing, and adhering to best practices in coding standards. Discuss how these practices have led to successful project outcomes.

Highlight your approach to mentorship, including providing constructive feedback, guiding them through complex problems, and encouraging them to take ownership of their projects. Sharing your own mentoring experiences can add authenticity to your response.

Describe your process for task prioritization, such as using tools to track priorities or communicating with team members to clarify deadlines. Providing an example where your prioritization led to successful project completion would strengthen your answer.

Discuss your familiarity with cloud services like AWS, GCP, or Azure, sharing specific examples of how you've leveraged these platforms in data processing or storage. This will demonstrate your adaptability in a modern data engineering role.

Emphasize the importance of clear communication and active listening when working with product owners. Describe how you gather requirements, manage expectations, and ensure alignment on project goals to facilitate successful outcomes.

Mention methods such as analyzing performance metrics, seeking feedback from users, and exploring new tools or technologies that can streamline workflows. Providing a specific example where you successfully improved a workflow would be compelling.

Detail your experience with ETL tools like Informatica, SSIS, or Azure Data Factory. Talk about specific projects where you utilized these tools, your role in the process, and how it benefited the data engineering efforts.

Discuss your strategies for managing tight deadlines, including effective time management, clearly defining scope, and making trade-offs while maintaining a focus on quality. Providing an example of a project where you successfully balanced these elements can enhance your response.