We are looking for a GCP Data Engineer to design build and maintain scalable data platforms on Google Cloud. The ideal candidate will have strong experience in automated data ingestion analytics engineering and MLOps with the ability to support marketing analytics use cases and BI-driven decision making.
You will work closely with Data Science Analytics and Business teams to enable data-driven insights through robust pipelines models and reporting solutions.
You will:
- Design develop and maintain automated data ingestion pipelines on Google Cloud Platform (GCP)
- Build and optimize scalable data processing workflows using Spark / PySpark
- Support data science team including data preparation feature engineering and experimentation support
- Develop deploy and maintain BI reporting and simulation tools using Looker
- Implement and manage MLOps pipelines for model training deployment monitoring and retraining
- Write efficient well-documented code in Python and SQL
- Ensure data quality reliability performance and governance across data pipelines
- Collaborate with cross-functional teams including Data Scientists Analysts and Product stakeholders
Qualifications :
- Strong hands-on experience with Google Cloud Platform (GCP)
- Experience supporting marketing analytics or attribution modeling use cases
- Familiarity with GCP services such as BigQuery Dataflow Dataproc Cloud Composer and Vertex AI
- Knowledge of CI/CD version control and cloud-native best practices
- Experience building automated data ingestion pipelines
- Proficiency in Python Spark PySpark and SQL
- Experience developing and deploying Looker dashboards and BI solutions
- Solid understanding of data engineering concepts: ETL/ELT data modeling and pipeline orchestration
- Exposure to MLOps pipelines and lifecycle management of machine learning models
- Ability to work with large-scale datasets and distributed systems
Remote Work :
No
Employment Type :
Full-time
We are looking for a GCP Data Engineer to design build and maintain scalable data platforms on Google Cloud. The ideal candidate will have strong experience in automated data ingestion analytics engineering and MLOps with the ability to support marketing analytics use cases and BI-driven decision ma...
We are looking for a GCP Data Engineer to design build and maintain scalable data platforms on Google Cloud. The ideal candidate will have strong experience in automated data ingestion analytics engineering and MLOps with the ability to support marketing analytics use cases and BI-driven decision making.
You will work closely with Data Science Analytics and Business teams to enable data-driven insights through robust pipelines models and reporting solutions.
You will:
- Design develop and maintain automated data ingestion pipelines on Google Cloud Platform (GCP)
- Build and optimize scalable data processing workflows using Spark / PySpark
- Support data science team including data preparation feature engineering and experimentation support
- Develop deploy and maintain BI reporting and simulation tools using Looker
- Implement and manage MLOps pipelines for model training deployment monitoring and retraining
- Write efficient well-documented code in Python and SQL
- Ensure data quality reliability performance and governance across data pipelines
- Collaborate with cross-functional teams including Data Scientists Analysts and Product stakeholders
Qualifications :
- Strong hands-on experience with Google Cloud Platform (GCP)
- Experience supporting marketing analytics or attribution modeling use cases
- Familiarity with GCP services such as BigQuery Dataflow Dataproc Cloud Composer and Vertex AI
- Knowledge of CI/CD version control and cloud-native best practices
- Experience building automated data ingestion pipelines
- Proficiency in Python Spark PySpark and SQL
- Experience developing and deploying Looker dashboards and BI solutions
- Solid understanding of data engineering concepts: ETL/ELT data modeling and pipeline orchestration
- Exposure to MLOps pipelines and lifecycle management of machine learning models
- Ability to work with large-scale datasets and distributed systems
Remote Work :
No
Employment Type :
Full-time
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