About Us
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
Staff Data Engineers are expert problem-solvers and builders who design, implement, and improve software applications and systems. In this role, engineers spend a significant portion of their time coding, working hands-on with code, data, and modern tools—including AI-assisted development, cloud services, and automation frameworks—to deliver secure, scalable, and high-quality technology solutions that drive business outcomes in the fintech sector. They collaborate with cross-functional teams such as product managers, designers, data scientists, QA, operations, and compliance to translate business requirements into robust technical solutions, all while adhering to best practices, security standards, and regulatory requirements.
This is a hands-on role requiring both deep data engineering expertise and the ability to work across legacy modernization and new platform innovation. You’ll collaborate closely with the Agentic Engineer, ML teams, and business stakeholders to enable AI-driven insights and intelligent data orchestration.
Key Responsibilities:
- Data Platform Modernization: Implement the transition from SQL Server data warehouse to the next-generation Hadoop/Databricks platform, ensuring performance, reliability, and minimal business disruption. Develop hybrid data pipelines that bridge legacy and modern ecosystems, enabling near real-time data access for analytics and AI applications. Optimize existing SQL Server models (facts, dimensions, indexes, stored procedures) and design modern equivalents in Hadoop and Spark environments. Define long-term migration strategy, data partitioning, and retention policies aligned with Visa’s data governance standards.
- Data Architecture & Engineering: Implement scalable, distributed data pipelines using Spark, Kafka, Airflow, and Delta Lake. Build robust ETL/ELT frameworks to process transactional, behavioral, and unstructured data at scale. Partner with the Agentic AI team to power RAG (Retrieval-Augmented Generation) pipelines, vector database integrations and LLM data provisioning. Lead proof-of-concept (POC) initiatives to evaluate and integrate new data engineering technologies.
- Operational Excellence: Provide guidance to junior Engineers to maintain existing SQL Server–based data warehouses, including database performance tuning, replication, encryption, and high-availability (HA) solutions. Implement best practices in T-SQL development, schema design, and stored procedure optimization. Perform proactive performance analysis, troubleshooting, and resolution of production issues in SQL Server and Hadoop clusters. Collaborate with DBA and application teams to ensure uptime within SLAs and compliance with Visa’s data policies.
- Security, Governance & Compliance: Enforce standards for data quality, lineage, and governance across both legacy and modern platforms. Ensure full compliance with Visa’s data privacy, encryption, and access control frameworks. Partner with InfoSec to embed data security principles into every phase of data lifecycle management.
- Leadership & Collaboration: Mentor and coach junior data engineers, fostering an environment of technical excellence and innovation. Collaborate cross-functionally with product, ML, and platform engineering teams on architecture decisions and roadmap execution. Serve as a bridge between legacy enterprise data teams and next-gen AI platform engineers, ensuring knowledge continuity and execution speed.
Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager.
Qualifications
Basic Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field.
- 8+ years of hands-on data engineering experience, including enterprise-scale data warehousing and pipeline design.
- Proven success supporting and modernizing SQL Server–based data warehouses in high-SLA environments.
- Production-level experience architecting Hadoop, Spark, and Databricks data pipelines.
- Expertise in ETL/ELT frameworks, data modeling, and schema design for analytical and operational use cases.
- Strong programming proficiency in Python, Java, or Scala.
- Hands-on experience with AWS or Azure (Glue, Synapse, Redshift, Delta Lake, S3).
- Familiarity with Kafka, Airflow, Kubernetes, and containerized data services.
- Understanding of RAG pipelines, vector databases, and AI data flows.
- Experience designing data pipelines and APIs compatible with Model Context Protocol (MCP)-based agent frameworks, enabling seamless integration between AI agents, data services, and enterprise APIs
- Strong SQL optimization, debugging, and production troubleshooting experience.
Preferred Qualifications:
- Experience developing data warehouse migration or modernization from SQL Server to Hadoop/Spark ecosystems.
- Deep understanding of data lineage, metadata management, and governance frameworks (e.g., Atlas, Great Expectations).
- Familiarity with LangChain, LangGraph, and MCP for integrating AI agents with data systems.
- Strong ability to balance innovation and stability across coexisting legacy and modern data architectures.
- Proven track record mentoring engineers and collaborating across global teams.
Leadership Attributes:
- Go-Getter: Executes decisively and thrives in complex, hybrid data environments.
- Builder: Hands-on developer who delivers scalable, production-grade data systems.
- Hustler: Operates with urgency and accountability across multiple technology stacks.
- Entrepreneurial: Drives innovation in data architecture and modernization.
- True North: Leads with integrity and alignment to Visa’s mission and long-term data strategy.
- Lead by Example: Sets high standards of excellence and transparency.
- Execute with Excellence: Ensures reliability, performance, and operational maturity across all data systems.
Tech Stack Snapshot:
- Languages: Python, Java, Scala, SQL, T-SQL
- Data Systems: SQL Server, Hadoop, Spark, Databricks, Delta Lake, Snowflake
- Pipelines: Kafka, Airflow, Glue, Azure Data Factory
- AI Integration: LangChain, LangGraph
- Cloud Platforms: AWS, Azure
- Infra: Docker, Kubernetes, Terraform, Jenkins
- Governance: Great Expectations, Atlas, Data Privacy & Encryption Frameworks
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.