Staff Data Engineer

Visa Technology and Operations LLC is hiring a Staff Data Engineer for the Value Added Services, Digital Marketing & Engagement organization. This hybrid role (Austin, TX) focuses on designing and advancing Visa’s data token platform, while leading large-scale data pipeline and data platform initiatives in an agile environment. You’ll work alongside product, analytics, and business teams to translate requirements into dependable, scalable data solutions that support analytics, reporting, and machine learning use cases.

What you’ll do

• Design, build, and optimize large-scale data pipelines for batch and real-time processing
• Develop and maintain distributed data systems that are highly available, scalable, and resilient
• Lead initiatives to process, transform, and manage structured and unstructured data across platforms
• Drive development of data platforms and data products for analytics, reporting, and machine learning
• Partner with product, analytics, and business teams to convert requirements into scalable data solutions
• Architect solutions using big data technologies including Hadoop, Spark, and distributed data processing frameworks
• Build and optimize ETL/ELT pipelines while ensuring high data quality, reliability, and performance
• Implement automation, monitoring, and observability across the full data pipeline lifecycle
• Lead proof-of-concept efforts to evaluate and adopt new data engineering technologies
• Influence and drive best practices across data engineering, including data modeling, pipeline design, and governance
• Mentor engineers and foster continuous learning and accountability
• Architect and implement secure, robust, and scalable data platforms
• Drive innovation through experimentation, prototyping, and rapid iteration
• Ensure strong data governance, quality, and operational excellence
• Collaborate with stakeholders to align technical delivery with data requirements and desired outcomes

What you’ll bring

• 5+ years of relevant work experience with a Bachelor’s Degree, or an alternative combination based on advanced degrees (Masters, MBA, JD, MD) and PhD experience as outlined
• Demonstrated leadership delivering high-quality, large-scale, enterprise-class applications
• Solid big data engineering experience, including Hadoop, Apache Spark, Python, and SQL
• Experience with Docker and Kubernetes for containerization
• Ability to create and manage large-scale data pipelines and machine learning models
• Experience developing ETL processes, maintaining Spark pipelines, and productizing AI/ML models
• Proficiency with technologies such as Kafka, Redis, Flink, TensorFlow, Triton, and AWS services
• Skilled in Unix/Shell or Python scripting and scheduling tools like Airflow and Control-M
• Familiarity with Agile development, TDD, CI/CD, and various databases
• Proven ability to build reliable, scalable, and operable applications
• Ability to manage component security analysis and collaborate with security teams
• Strong communication and interpersonal skills, with a collaborative, team-oriented mindset
• Commitment to mentoring and helping junior engineers grow

Technologies you may work with

Hadoop, Spark, ETL/ELT, distributed data processing frameworks, Python, SQL, Docker, Kubernetes, Kafka, Redis, Flink, TensorFlow, Triton, AWS services, Unix/Shell, Airflow, Control-M, Agile, TDD, CI/CD, databases

Compensation and benefits

Estimated salary range: USD 137,200 - 219,700 per year. Salary may vary based on job-related factors such as knowledge, skills, experience, and location.

• Medical
• Dental
• Vision
• 401(k)
• FSA/HSA
• Life Insurance
• Paid Time Off
• Wellness Program
• Potential sales incentive payments (if applicable)
• Bonus and equity (may be eligible)

Location, travel, and schedule

• Location: Austin, TX (hybrid)
• Travel: 5-10% of the time
• Office expectation: Visa requires at least 3 days in office; specific days will be confirmed by the hiring manager
• Work hours: Varies based on department needs

Work environment

• Office setting
• Requires sitting and standing at a desk, in-person and telephone communication, and frequent use of standard office equipment such as computers and telephones