Data Analyst

Role Overview

The Data Analyst turns data into reliable, reusable analysis that helps internal teams and lottery customers understand performance, identify opportunities, and make better operating decisions.

What You Will Do

Create and maintain analysis, reports, dashboards, notebooks, and visualizations that support revenue growth, cost reduction, operational performance, and customer success.

Why It Might Be a Fit

The role requires solid SQL skills, growing comfort with Python and SQL, familiarity with version control, and an interest in automating routine work so useful analysis can scale without adding unnecessary manual effort.

Requirements

• Bachelor’s degree in mathematics, economics, statistics, applied statistics, data science, computer science, engineering, or another quantitative field.
• At least three years of experience in business intelligence, data analysis, reporting, analytics engineering, data science, or a closely related role.
• Strong SQL skills, including joins, aggregations, filtering, validation, and the ability to work with data from multiple sources.
• Hands on experience, coursework, certification, or practical exposure with Databricks or comparable cloud analytics platforms, including notebook based analysis, lakehouse concepts, Spark SQL, or PySpark.
• Familiarity with cloud native analysis patterns in Azure, AWS, or Google Cloud, including shared workspaces, governed datasets, and scalable compute environments.
• Experience with Python or similar language for analysis, data preparation, automation, or reproducible analytical workflows.
• Familiarity with GitHub or similar version control tools, including commits, branches, pull requests or review workflows, and readable project documentation.
• Ability to design effective dashboards and visualizations in Tableau, Power BI, Looker, Sigma, or comparable BI tools.
• Strong attention to detail and ability to collect, organize, analyze, interpret, and communicate significant amounts of information accurately.
• Ability to explain analytical findings, tradeoffs, and recommendations in plain language for business, executive, and customer audiences.

Benefits

• Dental insurance
• Vision insurance
• Health insurance
• Paid time off
• Retirement plan
• Stock options

Tagged as: Data Analysis