Sr. Data Engineer - job 6 of 29

As a Sr. Data Engineer at Visa Technology & Operations LLC, you will engage in data engineering activities primarily within the Hadoop and Greenplum Data Warehouse ecosystem. Your main duties include working with large data sets, optimizing performance using MPP platforms, loading data from diverse sources, and ensuring compliance with evolving security and IT requirements.

To be considered for the Sr. Data Engineer role at Visa Technology & Operations LLC, candidates should possess strong proficiency in Big Data technologies, experience with Hadoop and GPDB, and a solid understanding of performance testing and debugging applications. Relevant educational qualifications in computer science or a related field, along with experience in a similar role, are also necessary.

A Sr. Data Engineer at Visa Technology & Operations LLC will have the opportunity to work on diverse projects involving data loading and transformation across disparate data sets, contributing to critical applications while addressing regulatory compliance and IT security needs. Collaboration with cross-functional teams will also be a key aspect of the projects undertaken.

At Visa Technology & Operations LLC, Sr. Data Engineers will enjoy a collaborative work environment, where team members can share ideas and best practices. The Austin office promotes flexibility with partial telecommuting options and encourages participation in project meetings to foster team engagement and project success.

Candidates applying for the Sr. Data Engineer position at Visa Technology & Operations LLC should be familiar with technologies such as Hadoop, Greenplum Data Warehouse (GPDB), and Massively Parallel Processing (MPP). Additionally, knowledge of programming languages such as Python or Scala, and experience with data integration tools, will be valuable.

When discussing your experience with Hadoop, highlight specific projects where you've utilized Hadoop for data processing or analytics. Be sure to mention any challenges you faced and how you overcame them, demonstrating your problem-solving skills and technical abilities in data engineering.

To ensure data quality, I implement rigorous testing and validation processes. I routinely check for consistency and accuracy in the data, leverage automated data quality tools, and keep an open line of communication with data source owners to address any discrepancies as soon as they're detected.

I use a combination of data partitioning, indexing, and optimizing resource allocation in a distributed computing environment to enhance performance. Additionally, regularly monitoring query performance and making adjustments based on usage patterns helps maintain system efficiency.

In a previous role, I was responsible for ensuring data compliance with security protocols. I implemented encryption for sensitive information, established access controls, and maintained thorough documentation to keep stakeholders informed. By actively monitoring for vulnerabilities, I helped safeguard our data assets against potential threats.

My approach involves replicating the issue, conducting a step-by-step analysis of the data processing steps, and using logging tools to track down where things went wrong. I always ensure I have a good understanding of the expected outcome versus the actual result, which allows me to pinpoint discrepancies and address them systematically.

I regularly participate in webinars, follow industry blogs, and engage in online communities focused on data engineering. Networking with other professionals at conferences and contributing to open-source projects also keeps me informed about the latest advancements and best practices in the field.

When discussing my experience with GPDB, I emphasize specific projects where I leveraged its analytic capabilities to extract insights from large datasets. I explain how I optimized queries for better performance and my role in maintaining the GPDB infrastructure within our data engineering team.

I rely on tools like Apache JMeter and QuerySurge for performance testing. These tools allow me to simulate load conditions and measure the performance of my data processing jobs, helping me identify bottlenecks before they become issues in production.

Collaboration is vital as it fosters knowledge sharing and brings together diverse perspectives, which can lead to innovative solutions. In data engineering, working alongside data scientists, analysts, and stakeholders often leads to more effective data strategies and ensures alignment with business goals.

What I enjoy the most about being a Sr. Data Engineer is the constant challenge of solving complex data problems and the satisfaction of transforming raw data into meaningful insights. It’s exhilarating to be at the forefront of technology, influencing decision-making through data.