Data Engineer- GCP

Job Title: Data Engineer GCP, PySpark & Scala We are looking for an experienced Data Engineer with 6+ years of experience in designing, developing, and maintaining scalable data engineering solutions on the Google Cloud Platform (GCP). The ideal candidate should have strong hands-on expertise in PySpark, Scala, Airflow, BigQuery (BQ), Dataproc, and ETL development. The candidate will be responsible for developing, deploying, monitoring, and optimizing batch ETL workflows in GCP environments. You should have experience building high-performance, reliable, and scalable data pipelines using PySpark and Scala, while leveraging Airflow for workflow orchestration, dependency management, scheduling, and idempotent execution. A strong understanding of Medallion Architecture (Bronze, Silver, Gold layers) is required, along with experience implementing data quality checks, data validation, summarization, and transformation processes. The role also requires expertise in performance tuning and optimization of ETL jobs, improving pipeline efficiency, and implementing observability solutions for proactive monitoring. Experience creating operational runbooks and supporting continuous optimization initiatives is highly desirable. The ideal candidate should be proficient in BigQuery, Dataproc, and other GCP services, with the ability to troubleshoot production issues and ensure high data reliability. Knowledge of data governance principles, metadata management, and best practices for secure and compliant data processing will be an added advantage. Mandatory Skills 6+ years of Data Engineering experience Google Cloud Platform (GCP) PySpark Scala Apache Airflow BigQuery (BQ) Dataproc ETL Development Medallion Architecture Data Quality & Validation Performance Optimization Observability Runbook Creation Good To Have Data Governance Metadata Management Production Support and Monitoring Skills: airflow,data,gcp,medallion Job Title: Data Engineer GCP, PySpark & Scala We are looking for an experienced Data Engineer with 6+ years of experience in designing, developing, and maintaining scalable data engineering solutions on the Google Cloud Platform (GCP). The ideal candidate should have strong hands-on expertise in PySpark, Scala, Airflow, BigQuery (BQ), Dataproc, and ETL development. The candidate will be responsible for developing, deploying, monitoring, and optimizing batch ETL workflows in GCP environments. You should have experience building high-performance, reliable, and scalable data pipelines using PySpark and Scala, while leveraging Airflow for workflow orchestration, dependency management, scheduling, and idempotent execution. A strong understanding of Medallion Architecture (Bronze, Silver, Gold layers) is required, along with experience implementing data quality checks, data validation, summarization, and transformation processes. The role also requires expertise in performance tuning and optimization of ETL jobs, improving pipeline efficiency, and implementing observability solutions for proactive monitoring. Experience creating operational runbooks and supporting continuous optimization initiatives is highly desirable. The ideal candidate should be proficient in BigQuery, Dataproc, and other GCP services, with the ability to troubleshoot production issues and ensure high data reliability. Knowledge of data governance principles, metadata management, and best practices for secure and compliant data processing will be an added advantage. Mandatory Skills 6+ years of Data Engineering experience Google Cloud Platform (GCP) PySpark Scala Apache Airflow BigQuery (BQ) Dataproc ETL Development Medallion Architecture Data Quality & Validation Performance Optimization Observability Runbook Creation Good To Have Data Governance Metadata Management Production Support and Monitoring Skills: airflow,data,gcp,medallion