Data Engineer
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
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Build and maintain data pipelines that transform source data into reliable inputs for machine learning use cases.
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Work closely with ML engineers to support feature creation and delivery for model training and inference workflows.
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Develop and improve data transformations used for feature generation, including pipelines that feed offline and online feature-related use cases.
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Ensure strong data quality assurance across pipelines by designing solutions that produce accurate, trustworthy, and well-monitored datasets.
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Take ownership of pipelines end to end, from implementation to maintenance, while continuously improving performance, scalability, and efficiency.
Requirements
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5+ years of experience in data engineering or a similar role.
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Strong hands-on experience building ETL/ELT pipelines with ownership from design to maintenance.
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Solid expertise in Apache Spark and Python, especially for large-scale data transformation workloads.
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Good understanding of SQL and practical experience working with data models and transformation logic.
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Experience working with high-volume or frequently running pipelines, including batch or near real-time processing scenarios.
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Ability to collaborate effectively with machine learning teams and understand data needs in ML-driven environments.
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Familiarity with Databricks is an advantage.
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Experience with streaming technologies (Flink, Kafka), feature stores, or ML-related data workflows is a strong plus.