Data Analyst

The Data Analyst role at Scientific Games focuses on turning data into dependable, reusable analysis that helps internal teams and lottery customers understand performance, uncover opportunities, and support better operational decisions. The position emphasizes clarity, accuracy, and repeatability across reporting, dashboarding, exploratory analysis, and recurring analytical workflows.

• Create and maintain analysis, reports, dashboards, notebooks, and visualizations that support revenue growth, cost reduction, operational performance, and customer success.
• Work with cloud native data platforms, with emphasis on Databricks, SQL, notebooks, governed datasets, and reusable workflows.
• Translate business questions into analytical approaches, including 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 appropriate visualization tool for each use case.
• 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 to reduce 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 communicate limitations, confidence levels, and recommended next steps.
• Create and maintain studies, research reports, executive dashboards, sales enablement tools, and exploratory visualizations that surface actionable insights.
• 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 by 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 maintain stakeholder alignment.
• Travel to lottery and corporate locations on an occasional basis as needed.

• Bachelor’s degree in a quantitative field, such as mathematics, economics, statistics, applied statistics, data science, computer science, engineering, or a related discipline.
• 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 working 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 a 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, including the ability to collect, organize, analyze, interpret, and communicate information accurately.
• Ability to explain analytical findings, tradeoffs, and recommendations in plain language for business, executive, and customer audiences.

• Experience contributing to reusable data models, metric layers, feature datasets, or certified analytical tables supporting multiple downstream use cases.
• Experience automating recurring reporting or analytics workflows using scheduled jobs, orchestration tools, notebooks, scripts, or CI style validation.
• Working knowledge of data quality practices, testing approaches, lineage, documentation, and governance in modern analytics environments.
• Experience with Alteryx, dbt, Airflow, Azure Data Factory, or similar transformation and orchestration tools.
• Familiarity with forecasting, segmentation, regression, classification, or other statistical methods used in…