About the role
Responsibilities:
- Design, develop, and deploy scalable ETL/ELT pipelines using PySpark on GCP.
- Utilise GCP services extensively, including BigQuery (data warehousing), Cloud Storage, Dataproc and Dataflow.
- Optimise PySpark jobs for performance and reliability, and fine-tune BigQuery queries.
- Implement complex transformations and process large volumes of structured/unstructured data using Spark SQL and PySpark.
- Build and manage automated workflows using Apache Airflow or Cloud Composer.
Requirements:
- Have implemented and architected solutions on Google Cloud Platform using the components of GCP.
- Experience with Apache Beam/Google Dataflow/Apache Spark in creating end-to-end data pipelines.
- Experience in some of the following: Big Query, Big Table Cloud Storage, Datastore, Spanner, Cloud SQL, and Machine Learning.
- Experience programming in Hadoop, PySpark/Python, SQL.
- Expertise in at least two of these technologies: Relational Databases, Analytical Databases, and NoSQL databases.
- Being certified as a Google Professional Data Engineer/Solution Architect is a major advantage.
- Mandatory Skills: Python, Pyspark, SQL, ETL, No SQL, GCP, BigQuery, Pub/Sub, Cloud Storage, Dataproc and Dataflow.
- Bachelor's or Master's degree in Computer Science, Engineering or a related field.
- Strong proficiency in Python and SQL is essential.
- Extensive hands-on experience with PySpark.
- Proven experience with Google Cloud Platform (GCP) services.