Director, Data Engineering

About UsVisa 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 Role

Summary:

The Director, Data Engineering leads the design, development, and deployment of scalable and secure data pipelines, platforms, and ML/AI infrastructure, leveraging cloud-native technologies and big data frameworks. This role oversees the integration of structured and unstructured data sources to support advanced analytics, machine learning, and AI initiatives, and champions new tools and technologies to improve productivity, scalability, and effectiveness of data engineering efforts.

The Director establishes and enforces data engineering best practices, technical standards, governance, and quality standards, ensuring continuous improvement and technical excellence, while ensuring data security, privacy, compliance, system performance, scalability, and availability across all data engineering projects and platforms.

All roles require digital fluency, including the ability to work with emerging technologies such as Generative AI tools (e.g. ChatGPT, Microsoft Copilot) to support everyday work.

Key Responsibilities:

Lead the design, development, and deployment of scalable and secure data pipelines, platforms, and ML/AI infrastructure.

Oversee integration of structured and unstructured data sources to support advanced analytics, machine learning, and AI initiatives.

Evaluate, implement, and champion new tools and technologies to improve productivity, scalability, and effectiveness of data engineering efforts.

Establish and enforce data engineering best practices, technical standards, governance, and quality standards.

Ensure data security, privacy, compliance, system performance, scalability, and availability across all data engineering projects and platforms.

Define, monitor, and report on key performance indicators (KPIs) to measure the success and impact of data engineering activities.

Lead incident response, root cause analysis, and resolution for data platform issues, ensuring high availability and reliability.

Collaborate with cross-functional teams to deliver robust data-driven business solutions and define technical roadmaps.

Mentor, coach, and grow a high-performing team of data engineers, fostering a culture of innovation, excellence, and continuous learning.

Partner with senior leadership to define, execute, and communicate the enterprise data strategy.

Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager.

Qualifications

Basic Qualifications:

10+ years of relevant work experience with a bachelor’s degree, or 13+ years of relevant work experience.

Preferred Qualifications:

12 or more years of work experience with a Bachelor’s Degree or 8-10 years of experience with an Advanced Degree(e.g. Masters, MBA, JD, MD) or 6+ years of work experience with a PhD.Experience in leading large-scale data engineering teams and projects in a complex enterprise environment.

Experience in data architecture, ETL/ELT pipelines, data warehousing, and real-time data processing.

Experience with big data technologies (e.g., Spark, Kafka, Hadoop).Experience in data modeling, data governance, and data quality frameworks.

Experience delivering high-impact data solutions that drive business outcomes.

Experience working with Data and AI, designing, and building ML infrastructure to train or serve models.

Experience with open-source data engineering tools or communities.

Hands – on development experience in building real time & streaming infrastructure and Dev Ops excellence.

Distributed Streaming Platforms:
Advanced architectural experience with large-scale event-driven systems using Apache Kafka, Apache Pulsar, AWS Kinesis, or Google Cloud Pub/Sub.

Stream Processing Engine Architecture:
Hands-on mastery or architectural oversight of real-time processing frameworks like Apache Flink, Apache Spark Streaming, Kafka Streams, and Ray.

Data Storage & Query Engines:
Deep knowledge of low-latency storage systems,…