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
