Data Engineer - Sr. Consultant level- Core Data Assets - job 5 of 21

As a Data Engineer - Sr. Consultant at Visa, your responsibilities encompass designing, coding, and testing data models, while also developing and overseeing data pipelines to guarantee quality and integrity. You'll be working closely with product delivery teams to implement proof of concepts and launch early products, alongside providing technical leadership for tooling and infrastructure. Additionally, your role will involve implementing AI and data science models for advanced data solutions, ensuring that our data operations run smoothly.

Visa seeks candidates for the Data Engineer - Sr. Consultant role who possess a solid background in data engineering, AI, and data science. Typically, a bachelor's or master's degree in a related field such as computer science, engineering, or information technology is necessary. Proficiency in database modeling, data pipeline design, and experience with languages like SQL, Python, or Java along with understanding of big data technologies are also crucial components of the required qualifications.

Collaboration is a key aspect of the Data Engineer - Sr. Consultant role at Visa. You'll partner with product delivery teams to effectively implement concepts and bring early products to life. By actively engaging with cross-functional teams, you will share your insights and expertise in data engineering, facilitating a collaborative environment that drives innovation and successful project execution.

In this hybrid position, you will alternate between working remotely and being in the office 2-3 days a week, as determined by leadership and business needs. This setup allows you to enjoy the flexibility of remote work while still collaborating in person with your team to foster strong relationships and enable more effective brainstorming and productivity. Visa values work-life balance and has designed this model to support that.

Visa offers robust career growth opportunities for Data Engineers at the Sr. Consultant level. You will gain access to various training programs and professional development resources to enhance your skills. Additionally, you will be positioned to take on leadership roles within projects, contribute to innovative technology talks, and present solutions across organizations. This growth trajectory is designed to nurture future leaders in the data engineering space.

To ensure data quality and integrity, I implement a series of validation, cleansing, and enrichment processes at multiple points along the data pipeline. I monitor data flows continuously and utilize automated checks to identify anomalies or discrepancies quickly, thus ensuring high-quality data for analysis.

Certainly! I have extensive experience with big data technologies such as Hadoop, Spark, and Kafka. For instance, while working on a project, I utilized Spark for processing large datasets efficiently, allowing for quicker insights and innovation in data-driven decision-making.

Performance optimization strategies include analyzing query patterns to streamline database indexing, leveraging partitioning for large datasets, and regularly evaluating and refactoring code for efficiency. I also use best practices for data structure design to ensure quick access to critical information.

I believe in open communication and regular check-ins with cross-functional teams. I proactively schedule meetings to align on project goals, share updates, and resolve any issues that may arise. Creating a platform for continuous feedback helps foster collaboration and drive project success.

I have worked on several projects that involved integrating AI and data science models within data platforms. For instance, I’ve developed machine learning models that automate scoring processes and further customized risk criteria based on historical data analysis, which significantly improved predictive accuracy.

When presenting to non-technical stakeholders, I focus on translating technical jargon into business value. I use analogies and visuals to simplify complex concepts, aiming to align the technical solutions with the stakeholders' goals and business outcomes to facilitate understanding.

I am proficient in SQL, Python, and Spark, which I have utilized effectively for data modeling, pipeline creation, and analytics tasks. Experience with tools such as Apache Kafka and REST APIs also aids in building robust data solutions that are scalable and efficient.

A synthetic data engine is crucial as it enables us to simulate realistic data scenarios without compromising the privacy of actual data. This allows for developing and testing data solutions and AI models safely, ensuring model robustness and reliability before deployment.

One of the main challenges I've faced is managing the vast amounts of data generated from various sources. I overcame this by implementing data governance practices that prioritize data quality and establish clear protocols for data ingestion and processing, ensuring consistency and reliability.

I stay updated by following industry-leading publications, participating in webinars, and engaging with online communities focused on data engineering. Attending conferences and networking with peers also provides insights into emerging tools and methodologies that can enhance my work.