GCP & PySpark with ETL - Lead
7 days ago
Key Responsibilities
- Design, develop, and optimize ETL pipelines using PySpark on Google Cloud Platform (GCP).
- Work with BigQuery, Cloud Dataflow, Cloud Composer (Apache Airflow), and Cloud Storage for data transformation and orchestration.
- Develop and optimize Spark-based ETL processes for large-scale data processing.
- Implement best practices for data governance, security, and monitoring in a cloud environment.
- Collaborate with data engineers, analysts, and business stakeholders to understand data requirements.
- Troubleshoot performance bottlenecks and optimize Spark jobs for efficient execution.
- Automate data workflows using Apache Airflow or Cloud Composer.
- Ensure data quality, validation, and consistency across pipelines.
- 5+ years of experience in ETL development with a focus on PySpark.
- Strong hands-on experience with Google Cloud Platform (GCP) services, including:
- BigQuery
- Cloud Dataflow / Apache Beam
- Cloud Composer (Apache Airflow)
- Cloud Storage
- Proficiency in Python and PySpark for big data processing.
- Experience with data lake architectures and data warehousing concepts.
- Knowledge of SQL for data querying and transformation.
- Experience with CI/CD pipelines for data pipeline automation.
- Strong debugging and problem-solving skills.
- Experience with Kafka or Pub/Sub for real-time data processing.
- Knowledge of Terraform for infrastructure automation on GCP.
- Experience with containerization (Docker, Kubernetes).
- Familiarity with DevOps and monitoring tools like Prometheus, Stackdriver, or Datadog.
Required Skills
Gcp,Pyspark,Etl
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