Staff Data Engineer at Visa

Foster City, California, United States -

Full Time

Start Date

Immediate

Expiry Date

25 Jun, 26

Salary

233700.0

Posted On

27 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Pipelines, Data Platform Architecture, Data Modeling, Streaming Data, Batch Processing, Cloud-Native, Distributed Systems, Python, Java, Scala, Spark, SQL, Parquet, Delta, Iceberg, Kafka

Industry

IT Services and IT Consulting

Description

Company Description Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid. At Visa, you'll have the opportunity to create impact at scale — tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world. Join Visa and do work that matters — to you, to your community, and to the world. Progress starts with you. Job Description Visa’s Technology Organization is a community of problem solvers and innovators reshaping the future of commerce. We operate the world’s most sophisticated processing networks capable of handling tens of thousands of secure transactions per second across millions of merchants, thousands of financial institutions, and billions of consumers worldwide. Our teams solve large-scale, complex problems across payments, data platforms, security, and next-generation digital experiences. The Opportunity: We are seeking a highly skilled and motivated Staff Data Engineer to join our Value Added Services Data Platform team in Foster City. In this role, you will be a senior technical contributor responsible for designing, building, and operating scalable data platforms that power analytics, reporting, and AI enabled products across Visa. You will own complex data engineering initiatives end to end, influence data platform architecture, and partner closely with Data Scientists, Product Managers, and Platform Engineers. You will play a critical role in ensuring data is reliable, performant, secure, and ready for advanced analytics and machine learning use cases. This role requires strong hands-on engineering expertise, sound technical judgment, and the ability to drive solutions across multiple teams. Essential Functions: Data Platform Engineering: Design, build, and maintain large-scale batch and streaming data pipelines that ingest, process, and serve high-quality data. System and Data Architecture: Lead the design of data models, schemas, storage formats, and access patterns to support analytics, reporting, and AI workloads. Data Quality and Observability: Implement data quality checks, monitoring, lineage, and alerting to ensure trusted and production-grade data assets. Cloud and Distributed Systems: Build and operate cloud-native data platforms using distributed processing frameworks and modern storage technologies. AI and Analytics Enablement: Partner with Data Science and AI teams to enable feature engineering pipelines, training datasets, and data flows for inference and analytics. Performance and Cost Optimization: Optimize pipelines and data storage for scalability, performance, reliability, and cost efficiency. Cross-Functional Collaboration: Work closely with Product, Engineering, Security, and Operations teams to deliver integrated and compliant data solutions. Platform Modernization: Drive adoption of modern data engineering practices including data versioning, schema evolution, CI/CD for data pipelines, and infrastructure as code. Automation and Reusability: Build reusable frameworks, libraries, and tooling to improve developer productivity and reduce operational overhead. Operational Excellence: Participate in on-call rotations, incident response, troubleshooting, and root cause analysis to maintain platform reliability. Mentorship and Technical Leadership: Mentor engineers, contribute to design reviews, and help raise the bar for data engineering standards and best practices. This is a hybrid position. Expectation of days in office will be confirmed by your hiring manager. Qualifications Basic Qualifications: 5+ years of relevant work experience with a Bachelor’s Degree or at least 2 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 0 years of work experience with a PhD, OR 8+ years of relevant work experience. Preferred Qualifications: 7 or more years of relevant work experience with a Bachelor’s degree in Computer Science, Engineering, or related field, or 5 or more years with an Advanced Degree. Strong hands-on experience designing and building large-scale data pipelines and data platforms in production environments. Expert proficiency in one or more programming languages such as Python, Java, or Scala. Experience with distributed data processing frameworks such as Spark or equivalent technologies. Strong SQL skills and experience with analytical and dimensional data modeling. Experience with modern data storage formats and technologies such as Parquet, Delta, Iceberg, or equivalent. Hands-on experience building and operating data platforms in cloud environments such as AWS, Azure, or GCP. Familiarity with streaming technologies such as Kafka or similar systems. Experience implementing CI/CD, version control, and automated testing for data pipelines. Strong understanding of data reliability, monitoring, observability, and operational best practices. Ability to support advanced analytics and machine learning use cases through well-designed data pipelines. Demonstrated ability to collaborate effectively across engineering, data science, and product teams. Strong troubleshooting, system design, and root cause analysis skills. Experience supporting machine learning and AI workflows including feature engineering, training data pipelines, and inference data flows. Exposure to MLOps concepts and data validation techniques for ML systems. Familiarity with containerization and orchestration technologies such as Docker and Kubernetes. Knowledge of enterprise data governance, privacy, and security best practices. Experience with real-time or near real-time analytics platforms. Experience in payments, financial services, or large-scale enterprise platforms is a plus. Proven ability to lead complex initiatives and influence technical direction across multiple teams. Additional Information Work Hours: Varies upon the needs of the department. Travel Requirements: This position requires travel 5-10% of the time. Mental/Physical Requirements: This position will be performed in an office setting. The position will require the incumbent to sit and stand at a desk, communicate in person and by telephone, frequently operate standard office equipment, such as telephones and computers. Visa is an EEO Employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law. Visa will consider for employment qualified applicants with criminal histories in a manner consistent with applicable local law, including the requirements of Article 49 of the San Francisco Police Code. U.S. APPLICANTS ONLY: The estimated salary range for this position is 146,200.00 to 233,700.00 USD per year, which may include potential sales incentive payments (if applicable). Salary may vary depending on job-related factors which may include knowledge, skills, experience, and location. In addition, this position may be eligible for bonus and equity. Visa has a comprehensive benefits package for which this position may be eligible that includes Medical, Dental, Vision, 401 (k), FSA/HSA, Life Insurance, Paid Time Off, and Wellness Program. Job Family Group: Engineering and Technology

Responsibilities

This senior technical role involves designing, building, and operating scalable data platforms for analytics, reporting, and AI products, requiring end-to-end ownership of complex data engineering initiatives. The engineer will ensure data reliability, performance, and security while partnering closely with Data Scientists, Product Managers, and Platform Engineers.