Data Insights Engineer

Job Description

Position Overview

We are seeking a highly skilled Data Insights Engineer to join our Visa University (VU) team.  VU is a global learning platform that provides immersive, practical training programs focused on the payments industry. You will play a critical role in generating actionable business insights, developing and optimizing data pipelines, and leveraging advanced analytics to solve complex business problems. This is a hands-on position combining analytical rigor with engineering excellence, suitable for candidates excited about both data science and data engineering

Key Responsibilities

Data Science and Analytics

Generate business insights from large and complex datasets, identify impactful recommendations, and communicate findings to clients and stakeholders
Brainstorm innovative ways to leverage Visa’s unique data assets to address key business challenges
Extract, aggregate, and analyze data using SQL, Spark, Hive, Airflow, or similar data pipeline tools
Build custom data models and algorithms- apply predictive analytics to optimize business outcomes
Develop compelling data visualizations to communicate sophisticated analyses to broad audiences using Power BI, Python, R, or similar tools
Assess and implement new data sources and data gathering techniques to improve analytical efficiency and accuracy
Data Engineering

Oversee the data lifecycle - acquisition, ingestion, transformation, storage, and governance for both streaming and batch data pipelines
Design, develop, and implement scalable, secure, and high-performance data pipelines, including reusable modules, automation tools, and frameworks
Ensure data security, privacy, governance, and compliance with all relevant regulations
Champion the adoption of GenAI and Agentic AI technologies into data pipeline workflows
Collaborate with business partners and cross-functional teams to deliver comprehensive, enterprise-level data solutions

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

Qualifications

Basic Qualifications
-5 or more 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

Preferred Qualifications
-6 or more years of work experience with a bachelor's degree or 4 or more years of relevant experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or up to 3 years of relevant experience with a PhD
- Bachelor’s degree in business Analytics, Statistics, Computer Science, Economics, Engineering, or a related field
- Minimum 3 years (Analyst/Scientist) or 3 years (Engineer) of relevant experience in data analytics, data science, or data engineering roles
- Proven experience with PowerBI, Tableau, SQL
- Proficiency in Python, R, SAS, or Stata for statistical analysis and modeling
- Experience with data visualization tools (Power BI, Python, R) and creating business-oriented presentations in PowerPoint
- Understanding of predictive and descriptive analytics, including regression, classification, clustering, and machine learning toolkits (Jupyter Notebook, Python, Hadoop)
- Experience optimizing code for computing efficiency and meaningful insight extraction
- The ideal candidate is a highly analytical individual with previous strategy, consulting and or financial experience
- Creative thinker with a results orientation who approaches any challenge with thoughtfulness, drive, and appropriate urgency
- Strong communication skills and ability to influence stakeholders and drive towards desired outcomes
- Aptitude to grasp new and complex concepts quickly
- Effective written and verbal communication skills (highly proficient with PowerPoint, Word, Excel, and Outlook)
- Excellent with communication, storytelling and presentation skills
- Exceptional analytical skills, with the ability to process and summarize large amounts of data into key strategic insights
- Demonstrated leadership in guiding teams, mentoring junior engineers, and establishing engineering best practices
- Experience developing and integrating GenAI or Agentic AI technologies into data workflows