Data Engineer
We’re looking for an experienced Data Engineer to build, scale, optimize, and maintain reliable data platforms. You’ll work on real-time pipelines, ML infrastructure, attribution data processing, and cloud-native solutions in a high-volume production environment.
Tech Stack
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GCP
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Apache Spark, dbt, BigQuery
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Python, SQL, Scala
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Apache Airflow
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Terraform, GitHub Actions, Docker, Kubernetes
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VertexAI
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Build and maintain large-scale advertising data pipelines for clicks, impressions, attribution, and event processing.
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Improve attribution modeling logic to support better optimization and business decisions.
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Ensure high availability, performance, stability, and scalability of real-time data workflows.
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Design and support production-grade ML model serving infrastructure.
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Develop and manage dbt datasets for model training, experimentation, and Feature Store integration.
Must Have
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Over 3 years of experience in Data Engineering and building production-scale data platforms.
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Strong programming skills in Python and SQL.
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Advanced experience with data modeling, ETL development, and multiple data formats.
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Strong knowledge of Google Cloud Platform and cloud-native architecture design.
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Expert knowledge of Apache Airflow for workflow orchestration and automation.
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Hands-on experience with Apache Spark for batch and high-volume streaming workloads.
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Good understanding of CI/CD and DevOps tools such as GitHub Actions or Kubernetes.
Nice to Have
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Experience with MLOps and ML deployment in production, preferably with Vertex AI.
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Practical experience with Terraform for Infrastructure as Code.
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Experience building scalable REST APIs for data or ML services.
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Strong testing practices, including TDD and unit/integration tests for Spark and Airflow.
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Knowledge of Scala for high-performance data processing.
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