Data Engineer - Senior Consultant level

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

We're building Visa's next-generation GenAI Platform - the intelligent data and orchestration foundation powering AI applications, copilots, semantic search, and agentic systems across the enterprise and eventually for Visa clients globally.

As a Data Engineer on the GenAI Platforms team, you will help architect and scale the data infrastructure that powers enterprise AI systems at global scale.

This is not a traditional data engineering role focused only on pipelines and warehousing.

You will work on AI-native data systems including retrieval infrastructure, vector indexing, semantic knowledge platforms, real-time context pipelines, orchestration data flows, and intelligent data services that enable large language models and AI agents to operate securely and effectively across enterprise environments.

You'll partner with software engineers, applied scientists, product teams, and platform architects to build highly scalable, production-grade systems that transform enterprise data into intelligent, context-aware AI experiences.

This role is ideal for engineers who enjoy:

• Building large-scale AI and data platforms from the ground up

• Solving complex distributed systems and data retrieval challenges

• Designing intelligent knowledge and context systems for LLMs and agents

• Working across streaming systems, APIs, orchestration layers, and cloud-native infrastructure

• Operationalizing GenAI systems in secure enterprise environments

You'll help define how enterprise AI systems access, retrieve, reason over, and operationalize data across one of the world's most trusted technology platforms.

What You'll Build

• Enterprise-scale retrieval and knowledge systems powering GenAI applications and AI agents

• Real-time and batch data pipelines supporting semantic search, embeddings, RAG, and orchestration workflows

• Intelligent context and indexing systems integrating structured and unstructured enterprise data

• AI-ready data infrastructure enabling scalable LLM applications and workflow automation

• Distributed streaming and event-driven architectures supporting AI-native applications

• Observability, evaluation, and governance systems for production AI data platforms

The Work Itself

• Design and build scalable data platforms supporting LLM applications, AI agents, semantic search, and retrieval-augmented generation (RAG)

• Develop high-throughput real-time and batch data pipelines integrating enterprise systems, APIs, documents, events, and knowledge sources

• Build vector indexing, embedding pipelines, semantic retrieval systems, and intelligent context management frameworks

• Engineer backend services and APIs enabling orchestration workflows, AI tool integrations, and enterprise automation use cases

• Develop scalable data ingestion and transformation frameworks for structured and unstructured enterprise data

• Optimize performance, reliability, latency, and scalability of distributed AI data systems operating at enterprise scale

• Implement observability, lineage, monitoring, and evaluation frameworks for AI-powered data platforms

• Partner with product managers, software engineers, data scientists, and platform teams to deliver secure, production-grade AI capabilities

• Contribute reusable frameworks, platform tooling, and engineering best practices accelerating enterprise GenAI adoption

• Explore emerging technologies across GenAI infrastructure, orchestration systems, vector databases, and cloud-native data platforms

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.

Preferred Qualifications:

*Four (4) years of experience solving data problems using data technologies (e.g., Hadoop, Hive, Kafka, Redis, NoSQL, RDBMS).

*Experiencebuilding enterprise GenAI platforms, semantic search systems, or AI data infrastructure

*Experience with vector databases, embedding pipelines, retrieval optimization, or knowledge graph systems

• Experience implementing observability, lineage, evaluation, and governance frameworks for AI-enabled data systems

*Familiarity with cloud-native AI infrastructure and scalable ML/data platform architectures

*Exposure to payments, fintech, or highly regulated enterprise environments with stringent security and reliability requirements

The Skills You Bring

AI-Native Data Engineering

Experience building data systems supporting LLM applications, RAG architectures, semantic retrieval, embeddings, vector databases, or AI orchestration workflows.

Distributed Data Systems

Strong expertise designing scalable distributed systems, streaming architectures, real-time pipelines, and large-scale data processing platforms.

Retrieval & Knowledge Infrastructure

Experience building semantic indexing systems, intelligent retrieval pipelines, metadata enrichment systems, or enterprise knowledge platforms.

Real-Time Data Engineering

Experience developing reliable event-driven and streaming systems using technologies such as Kafka, Spark, Flink, Hadoop, or similar large-scale processing frameworks.

Production Platform Engineering

Experience operationalizing secure, observable, and resilient production systems with strong focus on scalability, monitoring, governance, and reliability.

Backend & API Engineering

Ability to build backend services, APIs, orchestration integrations, and cloud-native components enabling intelligent applications and AI workflows.

Cloud-Native Infrastructure

Experience with Kubernetes, containerized deployments, CI/CD pipelines, infrastructure automation, and cloud platforms supporting distributed AI workloads.

Data + AI Systems Thinking

Ability to think beyond traditional ETL pipelines and design intelligent systems that provide context, retrieval, memory, and reasoning capabilities for AI applications.

Builder Mentality

Comfort operating in fast-moving environments with evolving AI technologies, ambiguous problem spaces, and platform-scale engineering challenges.

U.S. Applicants Only

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