Data Engineer

Aristocrat Gaming•Austin, TX

20h•$91,000 - $169,000•Onsite

About The Position

At Aristocrat, we are on a mission to bring happiness to life through the power of play! As a leading entertainment and content-building company, we are powered by technology and innovation, delivering world-class mobile and casino games to millions of people daily. Our dynamic culture and goal-focused approach develop an inclusive environment where your contributions matter. Joining us as a Data Engineer, you'll play a pivotal role in driving our engineering team's success and ensuring flawless execution of our projects. This is your chance to be part of an ambitious team that values collaboration, technical excellence, and continuous growth!

Requirements

4+ years of professional experience in data engineering or a closely related field.
Strong SQL skills and hands-on experience with data modeling for analytics or reporting use cases.
Skilled in Python and Node.js for data processing and pipeline development.
Experience working with ETL orchestration platforms to develop, schedule, and manage production data workflows.
Solid understanding of ETL/ELT patterns and the trade-offs involved in crafting data pipelines at scale.
Familiarity with Linux environments for pipeline deployment and operational tasks.
Ability to work independently on well-defined problems, making sound technical decisions without close supervision.
Strong attention to detail and a genuine dedication to data quality and reliability.
Clear communicator who can explain technical decisions and data concepts to non-technical collaborators.

Nice To Haves

Experience with graph databases such as Neo4j is a plus but not required.

Responsibilities

Compose, build, and maintain scalable data pipelines that extract data from casino source systems, apply complex transformations, and load optimized datasets for analytics and downstream applications.
Write efficient, production-quality code in Python and Node.js that fulfills reliability, performance, and maintainability criteria.
Develop and maintain data models that accurately represent business domains and support analytical use cases.
Work with ETL orchestration tools to schedule, monitor, and manage workflows in production environments.
Optimize queries and data processing logic to handle large volumes of transactional data efficiently.
Ensure data integrity across the pipeline through validation, testing, and observability practices.
Investigate and resolve data quality issues, pipeline failures, and performance bottlenecks.
Collaborate with data scientists, analysts, and product teams to understand data requirements and deliver solutions that meet them.
Document pipelines, data models, and technical decisions to support team knowledge and maintainability.