Staff Data Engineer - job 40 of 50

As a Staff Data Engineer at Visa, you'll be responsible for leading a team of data engineers, managing and prioritizing projects, and translating business needs into effective data models. You’ll create and maintain scalable data pipelines, develop robust data lakes, and ensure operational excellence through CI/CD implementation. Collaborating with stakeholders to meet their data needs will also be a critical function of this role.

The ideal candidate for the Staff Data Engineer role at Visa should possess proficiency in big data tools such as Hadoop and Spark, and programming experiences using languages like Scala, Python, and SQL. Strong analytical skills and experience with both structured and unstructured datasets are essential. Additionally, a background in managing large-scale data engineering projects and team leadership is highly valuable.

In the Staff Data Engineer position at Visa, you will play a crucial role in data integration by developing and maintaining data lakes. This includes acquiring data from various sources, building scripts for flexible data evaluation, and ensuring that data meets business quality standards through effective testing and load management procedures.

Absolutely! The Staff Data Engineer at Visa not only leads a team of data engineers but also plays a key role in mentoring them. You will coordinate with other teams to manage projects effectively, ensuring that the data engineering strategy aligns with the organization’s goals while fostering an environment of collaboration and growth within your team.

The work environment for the Staff Data Engineer at Visa in Atlanta is hybrid, allowing for flexibility in your work schedule. While the expectation of in-office days will be confirmed by your hiring manager, this setup promotes a balance between collaboration in-person and the benefits of remote work, catering to diverse working preferences.

To lead a team of data engineers effectively, it's crucial to establish clear communication and set concise goals. I adopt a collaborative approach by encouraging team input and regularly checking in on progress. Providing mentorship and fostering a culture of continuous learning also motivates the team and enhances their skills.

Prioritizing competitive data engineering projects requires a thorough understanding of business impact, deadlines, and resource availability. I typically collaborate with stakeholders to assess project urgency and value, allowing me to allocate resources and schedule projects effectively, ensuring we meet critical deadlines.

Certainly! In my previous role, I was responsible for designing robust data pipelines. This involved understanding data source requirements, selecting appropriate tools, and developing practices to ensure data quality. Regular testing and iterative improvements formed the backbone of my pipeline design to adapt to changing data needs.

I have extensive experience implementing CI/CD practices for data solutions, which include automating deployment processes and ensuring that our data pipelines and products are delivered consistently and reliably. This approach minimizes downtime and boosts operational efficiency while allowing for rapid iteration based on user feedback.

Ensuring data quality and integrity is fundamental in data engineering. I implement stringent testing protocols at various stages, conduct regular audits, and utilize automated tools to assess data against predefined business rules. This way, I can quickly identify and rectify any issues before they impact users.

In a previous role, I collaborated with several departments to understand their data needs for a new product launch. By hosting workshops and feedback sessions, we gathered comprehensive requirements, which helped us deliver a tailored data solution that met their expectations and significantly improved their operational efficiencies.

I maintain an agile mindset towards learning new technologies in data engineering by regularly participating in online courses, attending industry conferences, and engaging with professional communities. Staying updated with the latest trends not only enhances my expertise but also enables me to bring innovative solutions to my projects.

Managing a data lake presents various challenges, such as ensuring consistency across different data sources and maintaining performance efficiency. I tackle these challenges by implementing robust data governance frameworks and utilizing tools that allow real-time monitoring and optimization of data lake performance.

When encountering conflicting priorities, I prioritize open dialogue with the involved teams to understand their needs and constraints better. By facilitating discussions and identifying common goals, I can negotiate and reallocate resources effectively to address the most critical business objectives.

Documentation plays a vital role in my data engineering processes—it ensures that all team members are aligned, fosters knowledge sharing, and provides a reference point for future tasks. I emphasize clear documentation to capture processes, architectural decisions, and data lineage, which aids in onboarding and maintaining project continuity.