Lead Data Engineer

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 Visa's Technology Organization is a community of problem solvers and innovators reshaping the future of commerce. We operate one of the world's most sophisticated global transaction networks, processing more than 65,000 secure transactions per second across 80 million merchants, 15,000 financial institutions, and billions of people worldwide. Within ValueAdded Services (VAS), we are building AInative data foundations that transform how data is discovered, understood, and activated—enabling product intelligence, growth analytics, and real‑time decisioning across Visa's ecosystem. Opportunity We are seeking a Lead Data Engineer to define and drive the vision for VAS Data Foundations—a hybrid, AI‑ready platform that consolidates company‑wide data into a unified, governed, and intelligent data layer. This role sits at the intersection of data architecture, AI/ML, and product intelligence. You will shape how data flows from raw signals to semantic meaning to AI‑driven insights—powering dashboards, product analytics, AI agents, and executive decision making at global scale. Responsibilities Define the end‑to‑end architectural vision for AInative data foundations, spanning cloud data platforms, semantic layers, AI metadata, and consumption layers. Lead consolidation of disparate data sources (on‑prem, Hadoop, acquisitions, cloud) into a single, accessible, governed data layer. Design and evolve an AInative semantic layer that enables any agent, analyst, or product to discover, query, and reason over data consistently. Enable agentic and self‑service analytics, including automated insights, metric discovery, product analytics, and "talk to data" experiences. Partner with Product, Business, AI, and Data Science teams to operationalize AI Data Scientist capabilities for automated visualization, deep‑dive insights, and product intelligence. Establish architectural standards for data quality, metadata, lineage, observability, governance, and responsible AI usage. Guide platform evolution across data ingestion, ETL/ELT pipelines, scalable data models, metric catalogs, and presentation layers. Translate business and product needs (growth, churn, retention, fraud, performance) into durable data and semantic architectures. Influence and mentor senior engineers and architects, elevating architectural rigor and long‑term thinking across VAS. Essential Functions Advise on technical specifications during discussions with collaborators (e.g., Product owners, business partners, architects) to clarify sophisticated technical or business requirements and identify business needs and upstream/downstream systems. Define technical standards for the design and document the architecture for a complex product, using existing architecture design patterns. Provide technical expertise and mentor others to implement extensible, maintainable, and reusable code; define framework, principles, coding patterns, guidelines, and standard methodologies; and adhere to all security requirements. Oversee and establish unit testing requirements; act as subject‑matter expert in testing for coding standards and security scans; strategically lead user acceptance testing in collaboration with customers across multiple domains. Develop strategies and lead the team's automation efforts to drive efficiencies across data extraction, ensuring data quality and completeness using data wrangling, complex data modeling, and artificial intelligence; ensure adherence to data management principles, governance, process, and tools. Identify complex trends across relevant data sources and use insights to plan platform‑wide future solution updates; identify opportunities and define roadmap for software upgrades and server patches for security remediation where applicable. Qualifications Basic Qualifications 10 years of relevant work experience with a bachelor’s degree. 8 years of relevant work experience with an advanced degree (e.g., Masters, MBA, JD, MD). 3 years relevant work experience with a PhD. Preferred Qualifications Six (6) years of experience solving data problems using data technologies (e.g., Hadoop, Hive, Kafka, Redis, NoSQL, RDBMS). Four (4) years of experience designing, implementing, and maintaining ETL pipelines. Three (3) years of experience building and pushing code into production. Expert in at least one of the following: Golang, Java, or C/C++. Expert with web service standards and related patterns (REST, gRPC). Experience developing large‑scale, enterprise‑class distributed systems or subsystems that require high availability, low latency, and strong data consistency. Experience implementing solutions for low‑latency, distributed services using open standard technologies. Experience with Big Data and analytics in general leveraging technologies like Hadoop, Spark, Flink, and MapReduce. The Skills You Bring Strong data engineering mindset, with experience designing large‑scale data platforms, semantic layers, or analytics foundations. Deep understanding of cloud data ecosystems (data lakes, warehouses, streaming, ETL/ELT, metadata, governance). Experience working with or enabling AI/ML and LLM‑based analytics, including agent‑driven or conversational data experiences. Ability to think in systems and abstractions—data models, metrics, semantics, and contracts—not just pipelines. Comfort operating in ambiguity, shaping vision, and driving alignment across engineering, product, and business teams. Strong communication skills—able to explain complex architectures to senior technical and non‑technical stakeholders. Passion for building platforms that enable product analytics, growth insights, and decision intelligence at scale. Visa requires at least 3 days in office; expectations of these days will be confirmed by your Hiring Manager. Hybrid Work Arrangement This is a hybrid position. Hybrid employees can alternate time between remote and office. Employees in hybrid roles are expected to work from the office 2–3 set days a week (determined by leadership/site), with a general guidepost of being in the office 50% or more of the time based on business needs. Travel Requirements This position requires travel 5–10% of the time. Work Hours Varies upon the needs of the department. 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 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 the EEOC guidelines and applicable local law. #J-18808-Ljbffr