Data Analyst

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. This role supports reporting, dashboarding, exploratory analysis, and recurring analytical workflows with an emphasis on clarity, accuracy, and repeatability.

The analyst works with business partners, technical teams, and customer stakeholders to define questions, prepare and validate data, build cloud native analysis in environments such as Databricks, and communicate findings in clear business language. The role requires solid SQL, 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.

Key Responsibilities

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

Use cloud native data platforms, with emphasis on Databricks, SQL, notebooks, governed datasets, and reusable workflows.

Help translate business questions into clear analytical approaches, data requirements, success measures, and decision ready recommendations for internal stakeholders and external lottery customers.

Build and maintain dashboards, reporting assets, and self service data products using the right visualization tool for the need. Tableau experience is helpful, but the role is tool agnostic and values strong visualization judgment over a specific platform version.

Use Git Hub or similar version control practices to manage analytical code, notebooks, documentation, peer review feedback, and reproducible project history.

Automate recurring analysis, quality checks, data preparation, and distribution steps where practical so the team can deliver more value without adding proportional manual effort.

Partner with data engineering and business teams to improve data collection, transformation logic, metric definitions, and the usability of shared datasets.

Review data quality, reconcile source differences, document assumptions, and clearly communicate limitations, confidence levels, and recommended next steps.

Create and maintain studies, research reports, executive dashboards, sales enablement tools, and exploratory visualizations that surface useful insights and practical growth opportunities.

Identify process improvement opportunities and contribute to durable solutions that reduce rework, improve trust in the data, and make future analysis easier to repeat.

Manage multiple analytics tasks and projects, balancing stakeholder requests with clear documentation and reliable analytical practices.

Work effectively in a remote environment, using collaboration tools, written updates, and shared documentation to keep stakeholders aligned.

Travel to lottery and corporate locations on an occasional as needed basis.

Bachelor's degree in mathematics, economics, statistics, applied statistics, data science, computer science, engineering, or another quantitative field. A master's degree in data science, applied statistics, analytics, or a related field is a plus.

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 work spaces, governed datasets, and scalable compute environments.

Experience with Python or similar language for analysis, data preparation, automation, or reproducible analytical workflows.

Familiarity with Git Hub 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…