Job Description And Responsibilities

Visa’s future is powered by data. Our ability to responsibly harness data will shape our strategies, products, policies, and long‑term growth. As part of this mission, Visa is accelerating its data‑driven capabilities with a bold vision led by Visa’s first Chief Data Officer and the Global Data Office (GDO). The GDO leads high‑impact, company‑wide initiatives in data strategy, data investments, policy development, and marketplace execution—working closely with Technology, Privacy, and all global lines of business. We are committed to advancing innovation while upholding Visa’s Data Values and ensuring trusted, privacy‑centric use of data worldwide.

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Within the Global Data Office, the Information Consumption Product Team is responsible for building, optimizing, and scaling Visa’s unified data platform—the foundation that powers analytics, AI/ML, decisioning systems, and data‑driven products across the organization. Our team partners with engineering, product, and business stakeholders to deliver secure, scalable, high‑quality capabilities that support internal and external data consumers.

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As a Analyst (Data Engineering), you will play a pivotal role in shaping the next‑generation data foundation that fuels secure collaboration, analytics, and innovation at Visa. You will bridge product strategy with technical execution, ensuring that platform capabilities align with business needs while meeting high standards for security, privacy, and regulatory compliance. This role offers the opportunity to influence Visa’s global data‑sharing strategy, support privacy‑centric collaboration, and drive adoption of cloud‑native and generative AI–enabled data engineering practices.

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Key Responsibilities:

• Translate business and product requirements into scalable, secure data engineering solutions across cloud and hybrid environments.
• Design and implement data ingestion, transformation, sharing, and quality pipelines leveraging cloud‑native platforms.
• Build and scale data clean room capabilities that enable multi‑party collaboration while meeting privacy, governance, and regulatory needs.
• Develop reusable data services, APIs, and frameworks that enable unified data access across cloud and on‑premise systems.
• Optimize platform performance, storage, compute, and data‑transfer efficiency across distributed systems.
• Implement and maintain robust data security, governance, access controls, and compliance frameworks.
• Partner with data scientists, product managers, business, engineers, architects, and analytics teams to deliver data‑as‑a‑service (DaaS) capabilities.
• Lead the modernization of legacy Hadoop pipelines and datasets into cloud‑native architectures.
• Provide mentorship on data engineering standards, design principles, quality frameworks, and best practices.
• Apply Generative AI (GenAI) to automate code development, documentation, pipeline optimization, and data lifecycle workflows.

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This is a hybrid position. Expectation of days in office will be confirmed by your hiring manager.

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Qualifications

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Basic Qualifications:
Bachelor’s degree in Computer Science, Data Analytics, Data Engineering, or a related technical field with 4+ years of relevant experience,

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Preferred Qualifications:
Technical Expertise
4+ years of professional experience with a Bachelor’s degree in Computer Science, Data Engineering, Analytics, or similar.
Strong hands‑on experience (3+ years) in data engineering with the ability to support product analytics workflows and collaborate closely with product teams.
Proficiency in distributed data processing technologies such as Apache Spark, Hadoop, and modern data engineering languages (Python, SQL, Pandas, PySpark).
Experience supporting or building secure data collaboration solutions—Data Clean Rooms, privacy‑preserving sharing, governance frameworks, and controlled data‑access models.
Skilled in ETL/ELT development, workflow orchestration (Airflow, Dagster, Prefect), and distributed query engines (Presto, Trino, Drill, Athena).
Strong understanding of cloud‑native data architecture (AWS, Azure, or GCP)—object storage, compute frameworks, data lake/lakehouse patterns, Delta Sharing, IAM, encryption.
Familiarity with synthetic data generation, ML workflows, and model lifecycle considerations.
Advanced cloud certifications (AWS, Databricks, Snowflake etc.) are a plus.

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Product & Domain Knowledge:
Experience collaborating with product managers, analysts, and engineering teams to deliver scalable, insight‑driven data products.
Understanding of payments, financial data ecosystems, and regulatory requirements, prior domain experience is strongly preferred.
Strong command of data privacy, compliance standards (PCI, GDPR), and secure data‑access patterns.
Ability to translate ambiguous or evolving business needs into clear technical requirements and influence product direction using a data‑first perspective.

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Collaboration & Leadership:
Experience working in agile environments, driving cross‑functional alignment across engineering, analytics, product, compliance, and business stakeholders.
Excellent communication skills with the ability to present trade‑offs, guide data‑driven decisions, and support product planning.
Proven ability to design and support robust, reliable, and scalable data solutions, especially for high‑volume transactional or analytics platforms.
Creative, structured problem‑solver with the ability to operate in ambiguous environments and propose innovative, data‑driven solutions.