Sr. Data Engineer - job 9 of 26

As a Sr. Data Engineer at Visa Technology & Operations LLC, you will perform vital engineering activities within the Hadoop and Greenplum Data warehouse ecosystem. Your responsibilities include working with large datasets, leveraging Massively Parallel Processing (MPP), optimizing data loads from disparate sources, and ensuring compliance with evolving security regulations. You'll also be involved in team collaborations and lead project meetings to convey vital insights and updates.

To qualify for the Sr. Data Engineer position at Visa Technology & Operations LLC, candidates typically need a strong background in data engineering with experience in Hadoop, Greenplum Data warehouse, or similar technologies. Expertise in Big Data tools, data loading strategies, performance testing, and debugging applications is crucial. Additionally, a solid understanding of IT security compliance is essential, along with excellent communication skills to represent the team effectively.

Visa Technology & Operations LLC supports its Sr. Data Engineers through a collaborative work environment that encourages knowledge sharing and continuous learning. Team members will have access to the latest Big Data technologies and tools, empowering them to tackle challenges creatively. The company also offers flexible work arrangements, allowing a balance between on-site collaboration and remote work, enhancing job satisfaction and work-life harmony.

In the Sr. Data Engineer role at Visa Technology & Operations LLC, you'll primarily work with Hadoop and the Greenplum Data warehouse ecosystem. Familiarity with Massively Parallel Processing (MPP) platforms, Big Data technologies, and tools for data transformation and loading will be essential. Additionally, understanding security protocols and compliance measures will play a significant role in your daily tasks.

Yes, the Sr. Data Engineer position at Visa Technology & Operations LLC offers partial telecommuting options. While the role will primarily be based in the Austin, TX office, the company understands the importance of flexibility and allows its team members to balance their professional commitments while working from home when needed.

To handle large datasets effectively, I utilize cloud-based platforms or data warehousing solutions that allow for scalable storage and efficient queries. Proper indexing, partitioning, and leveraging batch processing techniques help improve performance. I also focus on optimizing SQL queries to ensure fast data retrieval. Additionally, I've implemented data validation processes to maintain data quality during uploads.

My experience with Hadoop includes building and maintaining data pipelines that ingest data from various sources into the Hadoop ecosystem. For Greenplum Data warehouse, I've designed tables and created efficient ETL processes to transform and load large datasets. I also have hands-on experience performing performance tuning and monitoring to ensure optimal operation.

When debugging data pipeline issues, I start by analyzing log files to identify where the failure occurred. I systematically isolate different stages of the pipeline and test each component individually. I utilize monitoring tools to track data flow and identify bottlenecks. I also advocate for maintaining clear documentation throughout the development process to streamline debugging.

I ensure data security and regulatory compliance by familiarizing myself with applicable regulations such as GDPR and CCPA. I implement data encryption techniques and ensure that access controls are in place to limit data exposure. Regular audits and security assessments are conducted to identify vulnerabilities. Staying updated on best practices in data security is also essential in my approach.

My experience with performance testing has involved creating test cases to evaluate the speed and efficiency of data loading and querying processes. I use profiling tools to measure execution times and identify performance issues. Based on test results, I make data model adjustments and optimize query execution plans to achieve significant performance improvements.

One of the most challenging projects I led involved migrating a legacy data warehouse to a modern cloud-based solution. This required extensive planning to minimize downtime and ensure data integrity. I collaborated with cross-functional teams to map out data flows and conducted thorough testing to validate the migration. The successful completion of this project significantly improved our data processing speeds and reporting capabilities.

I stay up-to-date by reading industry-related articles, participating in webinars, and attending conferences focused on data engineering and big data technologies. I also engage in online forums and follow thought leaders on social media platforms to stay informed about emerging tools and best practices in the field.

For data transformation and cleansing, I use ETL tools that allow for schema mapping, data type conversion, and filtering out duplicates. I implement transformation rules based on business requirements to ensure clean data is loaded into data warehouses. Regular expressions and data profiling aid in identifying quality issues during the transformation process.

Collaboration with cross-functional teams starts with clear communication. I ensure to actively listen to inputs and requirements from business analysts, data scientists, and other stakeholders. Regular meetings and updates keep everyone aligned, while shared documentation ensures that everyone has access to the project's progress. I believe in fostering a collaborative environment where ideas can flow freely to solve complex problems effectively.

Success metrics for a data engineering project can include data accuracy, system performance (like query response times), data processing speed, and user satisfaction levels. Monitoring system availability and error rates also provides insights into the project's overall health and identifies areas for improvement.