Staff Data Engineer - job 42 of 50

As a Staff Data Engineer at Visa, your primary responsibilities will include leading and mentoring a team of data engineers, coordinating with various teams to prioritize projects, and driving the execution of data engineering strategies. You'll also be tasked with translating business needs into data models, creating scalable data pipelines, and maintaining robust data lakes. Additionally, you will implement and manage CI/CD pipelines, ensuring that our data solutions remain efficient and reliable.

To excel as a Staff Data Engineer at Visa, proficiency in big data tools such as Hadoop, Hive, and Spark is essential. You'll also need strong programming skills in Scala, Python, and SQL, along with excellent analytical capabilities for handling diverse datasets. Experience in defining data load procedures, implementing data strategies, and collaboratively working with stakeholders across the organization is also crucial.

At Visa, a Staff Data Engineer has an exciting career path ahead, characterized by opportunities for growth and professional development. After establishing your role and demonstrating your expertise in leading data engineering projects, you could potentially move into higher leadership roles, such as Data Architect or Engineering Manager, where you'll play a more strategic role in guiding the data vision for the organization.

The Staff Data Engineer position at Visa is a hybrid role, which means that while you will have days designated to work in the Atlanta office, there will also be flexibility for remote work. The exact number of days in the office will be confirmed by your hiring manager, giving you a balance between collaborative office work and the comfort of working from home.

To prepare for the Staff Data Engineer role at Visa, focus on strengthening your skills in big data technologies, programming languages like Python, Scala, and SQL, and familiarize yourself with data modeling and pipeline creation. Additionally, consider seeking out leadership opportunities in your current role to gain experience in mentoring and project management, as these skills will be highly valued in this position.

When prioritizing projects, I assess the business impact, urgency, and resource availability. I also engage with stakeholders to ensure I understand their needs, which helps me make informed decisions on what projects to tackle first.

Certainly! In my previous roles, I've designed data pipelines using tools like Apache Spark and AWS services, focusing on scalability and reliability. I ensure that data flows efficiently from source to destination while implementing error handling and monitoring to maintain data integrity.

Handling unstructured data involves using tools like Apache Hadoop for storage and leveraging frameworks such as Apache Spark for processing. I focus on defining a clear schema where possible and use text processing techniques to extract meaningful insights from the unstructured data.

Effective stakeholder management starts with communication. I hold regular meetings to gather requirements and provide updates, which helps in understanding their data needs better and ensures alignment on project goals.

Best practices for data quality management include implementing validation rules and automated testing procedures to catch errors early. Additionally, regular audits and feedback loops with data users help maintain high data quality standards.

In a recent project, we faced significant delays due to data integration challenges. To overcome this, I coordinated closely with the data sources and created detailed documentation to streamline the integration process, which ultimately led us to successfully deliver the project on time.

I emphasize continuous learning and professional development by organizing training sessions and sharing resources. Regular code reviews and promoting a culture of accountability also help uphold engineering best practices within the team.

I have extensive experience with tools like Hadoop for data storage, Hive for data querying, and Spark for data processing, which allows me to handle big data projects effectively and efficiently.

I stay updated on data engineering trends by following industry blogs, attending webinars, participating in forums, and connecting with other professionals in the field to discuss emerging technologies and best practices.

CI/CD pipelines are crucial for ensuring consistent and reliable data deployment. They allow for automated testing and integration, which helps in quickly identifying issues, enhances collaboration among team members, and accelerates the release of new features.