You will work with BigQuery, Dataproc, and Cloud Composer to implement ETL/ELT workflows and ensure efficient data ingestion and processing across the stack.
Qualifications
Hands-on PySpark expertise in building scalable data pipelines.
Strong experience with GCP data services for real-time and batch processing.
Experience with orchestrating data workflows and enterprise-grade data solutions.
Responsibilities
Design and implement scalable data pipelines on Google Cloud Platform (GCP).
Build and manage batch and real-time data ingestion frameworks using PySpark and Spark.
Develop and optimize ETL/ELT workflows using GCP services.
Work with GCP storage and processing services including BigQuery, Dataproc, and Cloud Composer.
Skills
PySpark
Spark
Google Cloud Platform
Tools
BigQuery
Dataproc
Cloud Composer
Job description
We are looking for an experienced GCP Data Engineer with strong PySpark expertise to design, build, and optimize scalable data platforms and pipelines on Google Cloud Platform (GCP).
The ideal candidate should have hands-on experience in GCP data services, real-time and batch processing, orchestration frameworks, and enterprise-grade data engineering solutions.
Responsibilities
Design and implement scalable data pipelines on Google Cloud Platform (GCP).
Build and manage batch and real-time data ingestion frameworks using PySpark and Spark.
Develop and optimize ETL/ELT workflows using GCP services.
Work with GCP storage and processing services including BigQuery, Dataproc, and Cloud Composer.