Data Engineer - Senior Consultant level

Visa•Bellevue, WA

6d•$152,200 - $243,700•Hybrid

About The Position

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.

Requirements

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.
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.

Nice To Haves

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

Responsibilities

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