Top Gaming Data Engineering Jobs

Design, build, and maintain scalable data pipelines and API integrations; implement data quality monitoring and alerting; troubleshoot data issues; collaborate with business stakeholders; support CI/CD processes and scale architecture for future needs.

Design, build, and maintain high-throughput data processing services, focusing on streaming and batch data systems to enhance player experiences.

The Data Engineer will help build modern data infrastructure, designing ETL pipelines, modeling datasets, and collaborating with teams for improved data quality and accessibility.

As a Data Engineer, you will build and maintain data pipelines, ensure data reliability, collaborate with teams, and drive improvements in data platforms while supporting analytics and decision-making.

Manage a team of data engineers, collaborate with product and analytics teams, set technical direction, and ensure data quality and compliance.

The Analytics Engineer will design and build self-serve data products, oversee the data lifecycle, ensure data quality, and integrate third-party data sources while leveraging AI tools for improved analysis.

Operate, optimize, and scale Better Collective’s DMP and contextual targeting solutions. Build and manage audience segments, ensure inventory suitability and scale, support sales and product with data-driven insights, collaborate with vendors and internal teams, and maintain privacy/compliance (GDPR, CCPA).

The Sr. Director of Data Platform oversees data and AI platforms to enhance Player, Creator, and Publisher experiences by enabling data-driven decisions. Responsibilities include strategic leadership, platform development, cross-functional collaboration, and driving innovation in data operations.

The Head of Lottery Data Engineering & Data Warehousing leads the strategy and delivery of the lottery data platform, ensuring quality, compliance, and efficiency in data processing and storage across jurisdictions and products.

As a Data Engineer, you'll develop data architectures and pipelines, manage scalable data processing, automate workflows, and ensure data quality within the Data Platform organization.