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 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.   Preferred Qualifications:   ·                Four (4) years of experience solving data problems using data technologies (e.g., Hadoop, Hive, Kafka, Redis, NoSQL, RDBMS).  ·                Experience building 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 pro