Lead Data Engineer

Visa•Foster City, CA

3d•$173,100 - $307,600•Hybrid

About The Position

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 Value‑Added Services (VAS), we are building AI‑native data foundations that transform how data is discovered, understood, and activated—enabling product intelligence, growth analytics, and real‑time decisioning across Visa’s ecosystem. 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.

Requirements

10 years of relevant work experience with a bachelor’s degree -or- 8 years of relevant work experience with an Advanced degree (e.g., Masters/MBA/JD/MD) -or- 3 years relevant work experience with a PhD.
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 system or subsystems that require high availability, low latency, & strong data consistency computing.
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.
Strong data engineering mindset, with experience designing large‑scale data platforms, semantic layers, or analytics foundations.
Identify systemic patterns across data quality, usage, performance, and analytics friction—and drive foundational improvements.
Deep understanding of cloud data ecosystems (data lakes, warehouses, streaming, ETL/ELT, metadata, governance).
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.

Nice To Haves

Experience working with or enabling AI/ML and LLM‑based analytics, including agent‑driven or conversational data experiences.

Responsibilities

Define the end‑to‑end architectural vision for AI‑native 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 AI‑native 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.
Advises on technical specifications during discussions with collaborators (e.g., Product owners, business partners, architect) to identify and clarify sophisticated technical or business requirements and identify business needs and upstream and/or downstream system/application
Defines technical standards for the design and documents the architecture for a complex product, using existing architecture design patterns.
Provides technical expertise and mentors others to implement extensible, maintainable, and reusable code; defines framework, principles, coding patterns, guidelines, styles, and standard methodologies; and adheres to all security requirements.
Oversees and establishes unit testing requirements of unit testing to confirm functional capability of code; acts as subject matter expert in testing for coding standards and security scans; strategically leads user acceptance testing in collaboration with customer across multiple domains.
Develops strategies for and leads team's automation efforts to drive efficiencies across data extraction and ensure data quality and completeness using data wrangling, complex data modeling, and artificial intelligence.
Ensures adherence to data management principles, governance, process, and tools to maintain data quality across products.
Identifies complex trends across relevant data sources and uses insights to plan platform-wide future solution updates.
Identifies opportunities and defines roadmap for software upgrades and server patches for security remediation where applicable.