Informacje o stanowisku

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

• GCP
• Apache Spark, dbt, BigQuery
• Python, SQL, Scala
• Apache Airflow
• Terraform, GitHub Actions, Docker, Kubernetes
  
• VertexAI

Must Have

• Over 3 years of experience in Data Engineering and building production-scale data platforms.
• Strong programming skills in Python and SQL.
• Advanced experience with data modeling, ETL development, and multiple data formats.
• Strong knowledge of Google Cloud Platform and cloud-native architecture design.
• Expert knowledge of Apache Airflow for workflow orchestration and automation.
• Hands-on experience with Apache Spark for batch and high-volume streaming workloads.
  
• Good understanding of CI/CD and DevOps tools such as GitHub Actions or Kubernetes.

Nice to Have

• Experience with MLOps and ML deployment in production, preferably with Vertex AI.
• Practical experience with Terraform for Infrastructure as Code.
  
• Experience building scalable REST APIs for data or ML services.
• Strong testing practices, including TDD and unit/integration tests for Spark and Airflow.
• Knowledge of Scala for high-performance data processing.
  

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

• GCP
• Apache Spark, dbt, BigQuery
• Python, SQL, Scala
• Apache Airflow
• Terraform, GitHub Actions, Docker, Kubernetes
  
• VertexAI

,[Build and maintain large-scale advertising data pipelines for clicks, impressions, attribution, and event processing. , Improve attribution modeling logic to support better optimization and business decisions. , Ensure high availability, performance, stability, and scalability of real-time data workflows. , Design and support production-grade ML model serving infrastructure., Develop and manage dbt datasets for model training, experimentation, and Feature Store integration.  Requirements: Google Cloud Platform, ETL, Airflow, Python, SQL, Spark, DevOps, CI/CD, Kubernetes, MLOps, AI, Scala, REST API, Terraform, VertexAI Additionally: Sport subscription, Training budget, Private healthcare, Flat structure, Small teams, International projects.

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