Data Engineer- Data Science Platform - job 4 of 5

As a Data Engineer at Visa, your primary responsibilities include building machine learning solutions to enhance merchant data, ensuring data quality, and collaborating with cross-functional teams to unleash the potential of merchant data. You will be instrumental in developing AI-powered tools that empower the merchant ecosystem, working with technologies like SQL, Python, and big data frameworks. Your role will also involve leveraging automation frameworks and understanding cloud services to optimize data workflows.

Visa prefers candidates for the Data Engineer position to have strong skills in SQL and Python, as well as experience with machine learning libraries such as scikit-learn and Pandas. Additionally, familiarity with big data technologies like Hadoop and Spark, along with expertise in modeling techniques and automation frameworks (Airflow, Metaflow), is highly valued. Candidates with experience in MLOps, DevOps, and cloud services (especially AWS) will stand out during the selection process.

The work environment for a Data Engineer at Visa is dynamic and collaborative. This position is hybrid, allowing you to split your time between remote work and the office. You can expect to work in a supportive office setting for 2-3 days a week, enabling effective teamwork while enjoying the flexibility of remote work. This hybrid model is designed to enhance work-life balance while fostering innovative project collaboration with colleagues.

For a Data Engineer position at Visa, the estimated salary range is between $105,800 and $149,550 per year. Salary considerations are based on various job-related factors, including your experience, skills, and location. Moreover, this role may offer bonuses and equity options, alongside a comprehensive benefits package that encompasses medical, dental, vision, and wellness programs, further enhancing your overall compensation.

Essential skills for a Data Engineer at Visa include proficiency in SQL and Python, along with knowledge of machine learning libraries such as scikit-learn and Pandas. Experience with big data tools like Hadoop and Spark is crucial as you will be implementing modeling techniques such as decision trees, SVM, and more. Understanding MLOps practices, automation frameworks, and cloud services (especially AWS) is also highly beneficial for success in this role.

When answering this question, showcase specific projects where you utilized SQL to manage and analyze data. Highlight your proficiency in writing complex queries, optimizing performance, and integrating SQL with other technologies in your workflow.

Discuss your strategies for ensuring data quality, including validation techniques, automated quality checks, and the use of tools like Airflow for monitoring data pipelines. Providing examples of challenges you've faced and how you overcame them can illustrate your problem-solving skills.

List the machine learning algorithms you know such as logistic regression, decision trees, or neural networks. Share specific examples of projects where you've implemented these algorithms, including the context, goals, and results achieved to demonstrate your hands-on experience.

Provide details about your experiences with big data frameworks, explaining how you used them for data processing and analysis. Discuss any specific projects where these tools were critical to achieving the project's objectives.

Highlight your understanding and experience of using automation frameworks like Airflow. Discuss how you've leveraged automation to streamline workflows, reduce errors, or improve efficiency within data pipelines.

Detail your experience with cloud platforms, especially AWS, emphasizing services such as SageMaker or EMR. Illustrate how you've implemented solutions or managed resources in the cloud to support data engineering tasks.

Discuss your adaptability and how you manage time between remote work and in-office collaboration. Mention specific practices you use to enhance productivity and stay connected with your team regardless of your work location.

Explain your understanding of data security principles and compliance standards, citing specific measures you take to protect sensitive information throughout the data engineering process.

Use the STAR method (Situation, Task, Action, Result) to provide a structured response, detailing a specific challenge in a prior role, the steps you took to resolve it, and the positive impact it had on your project or team.

Connect your passion for data engineering to Visa's mission and values. Discuss what excites you about leveraging data to enhance payment technologies, drive innovations, or create meaningful business outcomes that align with Visa's goals.