Staff Data Engineer

Visa Visa · Fintech · Bengaluru, India, IN

Staff Data Engineer at Visa, focusing on designing, implementing, and improving software applications and systems for the fintech sector. The role involves hands-on coding with data and modern tools, including AI-assisted development and cloud services, to deliver secure, scalable, and high-quality technology solutions. Responsibilities include translating requirements into architecture designs, writing and reviewing code, owning data products/pipelines, building automation tools, writing queries, ensuring data quality, and troubleshooting. Basic qualifications include experience in data pipelines, coding, testing, CI/CD, monitoring, and integrating AI/ML services. Preferred qualifications include experience with various data technologies, distributed systems, Gen AI, and cloud platforms.

What you'd actually do

  1. Act as a key participant in meetings with stakeholders to identify and clarify requirements and determine business needs.
  2. Independently understand data ecosystems, security, privacy, and retention requirements to support business and product features.
  3. Translate functional requirements into architecture designs for one or more components, leveraging existing architecture design patterns, and communicate these to Data Engineers.
  4. Implement extensible, maintainable, and reusable code using appropriate coding patterns, guidelines, and best practices; participate in code reviews to ensure standards are followed.
  5. Provide end-to-end ownership of data products or data pipelines, including mitigation of technical debt and documentation of errors or unexpected bugs.

Skills

Required

  • designing and implementing data pipelines
  • large-scale data processing jobs
  • writing, modifying, and reviewing high-quality, testable, and efficient code
  • developing unit, integration, and end-to-end tests with a focus on automation
  • building and maintaining CI/CD pipelines
  • deploying applications using containerization and orchestration tools
  • monitoring applications in production
  • troubleshooting issues using observability tools
  • integrating AI or ML services into applications
  • collecting and analyzing metrics to guide optimizations and improvements
  • partnering with stakeholders to clarify requirements
  • applying secure coding practices
  • complying with regulatory standards

Nice to have

  • solving data problems using data technologies (e.g., Hadoop, Hive, Kafka, Redis, NoSQL, RDBMS)
  • highly distributed, scalable, concurrent and low latency systems
  • DB2, MS SQL, MySQL or NoSQL
  • continuous integration tools such as Jenkins, Artifactory
  • data visualization and business intelligence tools like PowerBI and Tableau
  • Docker and Kubernetes
  • MSBI Technologies (SSIS, SSAS, SQL Servers)
  • Gen AI technologies
  • cloud solutions like MS Azure, AWS, Google Cloud
  • mentoring junior engineers
  • writing clear technical documentation

What the JD emphasized

  • AI-assisted development
  • Generative AI tools
  • integrating AI or ML services into applications