Analytics Engineer I, Burger King

Restaurant Brands International•Miami, FL

2d•Onsite

About The Position

As an Analytics Engineer I, you will be responsible for transforming raw operational data into structured, reliable, and reusable analytical datasets that power analytics, forecasting, and decision-making across the organization. This is a data-centric role that sits at the intersection of engineering, analytics, and the business. You will work primarily in Snowflake, using SQL to build and maintain analytical data models, define business-critical metrics, and ensure the integrity of the analytical layer. You will collaborate with other Data Engineers, Analysts, and business stakeholders to develop data assets that support initiatives aimed at driving EBITDA and strengthening the brand.

Requirements

Bachelor’s degree in computer science, engineering, information systems, or data science
Strong proficiency in SQL for data transformation and data modeling.
Experience working with Snowflake or similar cloud data warehouse technologies.
Familiarity with version control systems such as Git.
Understanding of data modeling concepts (e.g., building structured, reusable datasets).
Strong problem-solving and analytical skills.
Excellent communication and collaboration skills.

Nice To Haves

Basic proficiency in Python is a plus.
Familiarity with workflow orchestration tools such as Dagster, Airflow, or similar is a plus.
Familiarity with AWS environments (e.g., S3) is a plus.

Responsibilities

Design, build, and maintain structured analytical datasets using SQL to transform raw data into clean, business-ready tables.
Define and maintain standardized business metrics, ensuring consistency and accuracy across analytical use cases.
Implement and maintain data quality checks to ensure analytical datasets are accurate, reliable, and aligned with business rules.
Develop production-ready, version-controlled data assets that are reusable, maintainable, and support multiple analytical applications.
Partner with analysts and stakeholders to translate business needs into structured data models and analytical datasets.