Lead Data Architect – Global Client Services (Hybrid/Onsite)

Company Description

Visa is a world leader in payments and technology, with over 259 billion payments transactions flowing safely between consumers, merchants, financial institutions, and government entities in more than 200 countries and territories each year. Our mission is to connect the world through the most innovative, convenient, reliable, and secure payments network, enabling individuals, businesses, and economies to thrive while driven by a common purpose – to uplift everyone, everywhere by being the best way to pay and be paid.

Make an impact with a purpose-driven industry leader. Join us today and experience Life at Visa.

Job Description

We are seeking a senior data architect to lead our data science and engineering initiatives. 

In this strategic leadership role, you will be responsible for developing and executing a data strategy that unlocks the power of our data assets and drives innovation through the implementation of data engineering and GenAI solutions.

This role will be accountable for delivering modern, large-scale, complex services and products for the Global Client Services business. You are a trusted technology advisor and thought leader working with architects, DEV leads, test engineering leads, TPMs, product managers, security architects, and other partners across the organization in ensuring that what we build is secure, scalable, performant, and reliable. 

Responsibilities:

• Define and implement a data strategy that aligns with business objectives and maximizes the value derived from data assets.

• Lead the design and architecture of a robust and scalable data engineering platforms, infrastructure, including data pipelines, data warehouses, and data lakes (on-prem and cloud-based).

• Oversee the entire data lifecycle, from data acquisition and ingestion to transformation, storage, and analysis.

• Champion the adoption of GenAI technologies and develop strategies to integrate them into existing workflows or build new.

• Build and manage a high-performing data engineering team, fostering a culture of innovation and continuous learning.

• Partner with business stakeholders to understand their needs and translate them into actionable data-driven solutions.

• Ensure data governance and compliance with all relevant regulations and develop and implement data governance policies and procedures to ensure data security, privacy, and compliance.

• Stay up-to-date on emerging data and AI trends and technologies.

This is a hybrid position. Hybrid employees can alternate time between both 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.

Qualifications

Basic Qualifications
•10 or more years of work experience with a Bachelor’s Degree or at least 8
years of work experience with an Advanced Degree (e.g. Masters/
MBA/JD/MD) or at least 3 years of work experience with a PhD

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
•Bachelors or master’s in computer science, Operations Research, Statistics, or
highly quantitative field (or equivalent experience) with strength in Deep
Learning, Machine Learning, Data Mining, Statistical or other mathematical
analysis
•12-15 years of hands-on experience and a proven track record of building and
deploying complex data pipelines and architectures using various
technologies.
•Expertise in on-prem and cloud-based data platforms (AWS, Azure) and related
data storage and processing tools (e.g., Spark, Hadoop).
•Experience with data warehousing, data lakes, and business intelligence and
ETL tools.
•Strong proficiency in programming languages like Python and SQL.
•Relevant exposure to modeling techniques such as logistic regression, Naïve
Bayes, SVM, decision trees, or neural networks
•Expert in leading-edge areas such as Machine Learning, Deep Learning, Stream
Computing and MLOps
•Experience with Python, SQL, PySpark and Hive on data and analytics solutions
•Excellent understanding of algorithms and data structures
•Expertise with GenAI technologies (e.g., Large Language Models, NLP) and
vector databases. Experience with tuning open-source Large Language
Models (LLMs)
•Excellent communication skills with the ability to present complex ideas clearly
and concisely.
•Experience in setting and tracking technical goals, operational SLA/OLAs, and
KPIs.
•Excellent leadership and tea