Analytics Engineer

Mohegan•Uncasville, CT

23d•Onsite

About The Position

The Analytics Engineer builds and maintains the modeled data and metric definitions that power reporting, experimentation, and the future platform within Decision Intelligence. Working under the Analytics Engineering Manager, the incumbent develops trusted, well-documented datasets and dashboards, and grows into an increasingly independent contributor. The role is a strong fit for a capable problem solver who is early in their analytics-engineering career and eager to develop modeling, SQL, and data-visualization depth.

Requirements

Bachelor’s degree in a quantitative, computer science, statistics, information systems, or related field required.
1–3 years of experience in analytics, data, business intelligence, or a related role, including relevant internships or academic projects.
Working proficiency in SQL required.
Experience building data visualizations and dashboards (Tableau preferred; Power BI / Looker Studio acceptable).
Familiarity with data modeling concepts and a willingness to develop analytics-engineering depth (dimensional modeling, semantic layers, dbt-style transformations).
Strong analytical and problem-solving skills, with attention to detail and a demonstrated ability to learn quickly and work through ambiguous problems.
Excellent communication skills with the ability to translate data into marketing recommendations.
Intermediate knowledge of Microsoft Office Suite products and applications.
Ability to manage multiple tasks, take direction, and collaborate within a team.

Nice To Haves

Exposure to a modern data warehouse (e.g., Snowflake) and to Python for data work is a plus.
Interest in the online gaming / iGaming industry; direct experience is a plus but not required.

Responsibilities

Develop, test, and maintain data models and transformations (SQL, dbt-style workflows) on the data warehouse under the direction of the Analytics Engineering Manager.
Implement and document metric definitions in the semantic layer, and build reliable, well-organized datasets that others can self-serve from.
Build and maintain dashboards and reports (Tableau and related tools) that translate data into clear, accurate answers for business and marketing stakeholders.
Own data quality for assigned domains — monitor pipelines, investigate discrepancies, and help ensure a single source of truth.
Collaborate with business stakeholders, analysts, and data teams to gather requirements and deliver effective data solutions that support organizational objectives.
Support experimentation, performance measurement, and analytical initiatives by developing datasets and metrics that enable informed decision-making.
Create and maintain comprehensive documentation for data models, metric definitions, datasets, reporting processes, and analytics workflows.
Assist in identifying opportunities to improve data architecture, reporting efficiency, self-service analytics capabilities, and overall data accessibility.