Data Engineer

• Bachelor's degree, OR 3+ years of relevant work experience
• 2 or more years of work experience.
• Data Engineering:
• Bachelor's degree, OR 3+ years of relevant work experience.
• Strong programming skills in Python, Java, SQL, or similar languages.
• Experience building and operating data pipelines, integrations, ETL/ELT workflows, and data processing applications.
• Experience working with structured and unstructured datasets at scale.
• Experience with distributed data processing, event-driven architectures, or streaming technologies such as Kafka.
• Experience with cloud-native development and enterprise data platforms.
• Experience implementing data quality, governance, monitoring, and observability practices.
• Experience with CI/CD pipelines, automated testing, and production support.
• Generative AI & Data for AI
• Experience building or integrating AI-powered applications and services.
• Understanding of Large Language Models, prompt engineering, RAG, embeddings, vector search, and semantic retrieval.
• Experience integrating enterprise data sources with AI applications and workflows.
• Understanding of AI solution evaluation, monitoring, and optimization.
• Familiarity with AI security, responsible AI practices, and guardrails.
• Engineering Productivity:
• Experience using AI-assisted development tools such as GitHub Copilot, Copilot Agents, Claude Code, Cursor, Windsurf, or similar tools.
• Experience leveraging AI for software development, testing, debugging, code reviews, and documentation.
• Strong analytical and problem-solving skills with the ability to work in fast-paced and evolving environments.
• Strong communication and collaboration skills.
• Advanced AI Engineering:
• Experience building agentic AI solutions and intelligent workflows.
• Familiarity with LangChain, LangGraph, Semantic Kernel, Spring AI, CrewAI, AutoGen, or similar frameworks.
• Familiarity with Model Context Protocol (MCP), agent interoperability, and tool-calling architectures.
• Experience with AgentOps, LLMOps, or AI platform engineering practices.
• Data & Platform Technologies:
• Experience with Databricks, Snowflake, BigQuery, Synapse, Spark, or similar platforms.
• Experience with vector databases, knowledge graphs, enterprise search, or semantic retrieval technologies.
• Experience with Kubernetes, Docker, Infrastructure as Code, and cloud platform services.
• Domain Experience:
• Experience in Digital Marketing, Customer Engagement, Personalization, Analytics, Loyalty, or Digital Products.
• Experience building customer intelligence, audience segmentation, recommendation, measurement, or personalization solutions.
• Understanding of privacy, consent management, and responsible data use practices.
• Innovation & Technical Leadership
• Ability to evaluate and adopt emerging technologies and engineering practices.
• Experience contributing to architecture discussions, design reviews, and engineering best practices.
• Passion for innovation, experimentation, and continuous learning.

• Design, develop, and maintain scalable data pipelines, integrations, APIs, and platform services supporting Digital Marketing & Engagement solutions.
• Build and optimize batch, streaming, and event-driven data processing workflows.
• Develop reusable data assets, data services, and platform capabilities that improve data accessibility, quality, and scalability.
• Ensure data quality, reliability, governance, security, and operational excellence across platform components.

• Build and integrate AI-powered applications, workflows, and data services leveraging Large Language Models and Generative AI technologies.
• Implement Retrieval-Augmented Generation (RAG), semantic search, vector retrieval, and knowledge-access patterns to enable intelligent experiences.
• Develop integrations between AI models, enterprise data sources, APIs, and business applications.
• Support evaluation, monitoring, and continuous improvement of AI-enabled solutions.

• Apply guardrails, governance controls, and responsible AI practices when developing AI-enabled solutions.
• Implement controls to improve data protection, model safety, content quality, and secure access to enterprise information.
• Monitor and optimize token consumption, response latency, reliability, and operational costs.
• Support observability, telemetry, monitoring, and evaluation frameworks for AI workloads.

• Utilize AI-assisted development tools and coding agents to improve engineering productivity and software quality.
• Apply AI-assisted workflows across development, testing, debugging, documentation, and operational support activities.
• Evaluate emerging technologies, tools, and frameworks within data engineering, Generative AI, and intelligent automation.
• Contribute to innovation initiatives that improve platform capabilities and developer effectiveness.

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

Data Engineers are expert problem-solvers and builders who design, implement, and improve software applications and systems. In this role, engineers spend a majority 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.

All roles require digital fluency, including experience leveraging AI-assisted development environments and intelligent engineering tools throughout the software development lifecycle. Candidates should be comfortable utilizing technologies such as Claude Code, Codex, GitHub Copilot, Copilot Agents, AI-assisted IDE workflows, and enterprise AI platforms to improve productivity, quality, and innovation.