Data and Analytics Engineer, Corporate GenAI

About the position

Visa’s Corporate Generative AI (CGAI) team is seeking an experienced Data Engineer to join our team. This role focuses on designing, building, and maintaining scalable data infrastructure and pipelines to support data visualization, advanced analytics, business intelligence, and GenAI initiatives. The ideal candidate will have a strong background in data engineering, cloud platforms, automation, and data visualization, with a passion for enabling data-driven decision-making across the organization.

Responsibilities

• Design, develop, and maintain robust ETL/ELT pipelines using modern data engineering tools and frameworks.
• Build and optimize data lakes and data warehouses.
• Ensure data quality, consistency, and security across all data platforms.
• Implement data governance best practices and scalable data models.
• Design and maintain interactive dashboards and executive-level reports using tools like Tableau and Power BI.
• Collaborate with stakeholders to define KPIs and translate complex data into clear visual narratives.
• Provide visualizations of key product metrics to guide product and customer strategy.
• Partner with architects, analysts, and business stakeholders to understand data needs and deliver engineering solutions.
• Support executive reporting and analytics by enabling reliable data access and transformation.
• Monitor data pipeline performance and implement improvements for scalability and reliability.
• Automate recurring data processes and reporting workflows to enhance operational efficiency.
• Develop scalable data pipelines and workflows using various data sources and platforms.
• Build and manage scheduled reporting systems to ensure timely delivery of recurring insights.
• Design and implement robust ETL/ELT processes to support analytics and GenAI use cases.
• Provide comprehensive, high-level insights through dashboards tailored for leadership and executive stakeholders.
• Optimize data models and queries for performance, reliability, and maintainability.
• Integrate data engineering solutions with BI tools and platforms.
• Collaborate with DevOps and cloud teams to manage infrastructure-as-code and CI/CD pipelines.
• Monitor and analyze key metrics to support continuous improvement.
• Explore opportunities for automation and process enhancement in data reporting and analytics.
• Continuously evaluate and adopt new tools, frameworks, and best practices.
• Conduct root cause analysis and implement solutions to improve data reliability.
• Participate in code reviews and knowledge-sharing sessions.

Requirements

• 5+ years of relevant work experience with a Bachelor’s Degree or at least 2 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 0 years of work experience with a PhD, OR 8+ years of relevant work experience.
• Proficient with data lakes and warehouses (e.g., Snowflake, BigQuery, Redshift).
• Strong skills in data modeling, ETL/ELT pipelines, and data governance.
• Experience building automated data pipelines and reporting systems for BI and GenAI use cases.
• Expertise in BI tools (e.g., Tableau, Power BI) and data transformation tools (e.g., Tableau Prep, Power Query, DAX, M).
• Familiarity with low-code platforms, automation tools, and API integrations.
• Understanding of GenAI concepts, LLM tools, prompt engineering, and supporting infrastructure.
• Experience with real-time data processing (e.g., Kafka, Spark Streaming).
• Knowledge of vector databases, semantic search, and embedding models.
• Familiarity with CI/CD pipelines, infrastructure-as-code, and containerization (e.g., Docker, Kubernetes).
• Proficiency in Agile methodologies and tools (e.g., JIRA).
• Understanding of data security, encryption, and compliance standards.
• Ability to work with structured, semi-structured, and unstructured data.
• Strong communication and collaboration skills across cross-functional teams.

Benefits

• Medical
• Dental
• Vision
• 401 (k)
• FSA/HSA
• Life Insurance
• Paid Time Off
• Wellness Program