About the team
We are a mobile application where users earn rewards by playing games. Our Data Engine team is at the heart of this ecosystem, working in a high-impact environment to build the recommendation systems and ML models that drive our product's growth. You will join a specialized squad of Data and ML Engineers focused on optimizing user journeys and scaling our platform toward full profitability. We are a lean, high-ownership team where every engineer manages their pipelines from A to Z.
About the team
We are a mobile application where users earn rewards by playing games. Our Data Engine team is at the heart of this ecosystem, working in a high-impact environment to build the recommendation systems and ML models that drive our product's growth. You will join a specialized squad of Data and ML Engineers focused on optimizing user journeys and scaling our platform toward full profitability. We are a lean, high-ownership team where every engineer manages their pipelines from A to Z.
Responsibilities
Build and maintain data pipelines that transform source data into reliable inputs for machine learning use cases.
Work closely with ML engineers to support feature creation and delivery for model training and inference workflows.
Develop and improve data transformations used for feature generation, including pipelines that feed offline and online feature-related use cases.
Ensure strong data quality assurance across pipelines by designing solutions that produce accurate, trustworthy, and well-monitored datasets.
Take ownership of pipelines end to end, from implementation to maintenance, while continuously improving performance, scalability, and efficiency.
Requirements
5+ years of experience in data engineering or a similar role.
Strong hands-on experience building ETL/ELT pipelines with ownership from design to maintenance.
Solid expertise in Apache Spark and Python, especially for large-scale data transformation workloads.
Good understanding of SQL and practical experience working with data models and transformation logic.
Experience working with high-volume or frequently running pipelines, including batch or near real-time processing scenarios.
Ability to collaborate effectively with machine learning teams and understand data needs in ML-driven environments.
Familiarity with Databricks is an advantage.
Experience with streaming technologies (Flink, Kafka), feature stores, or ML-related data workflows is a strong plus.
