GCP Data Engineer

Job Title:

Location:

Experience:

About the Role:

• Design, build, and maintain large-scale data pipelines on BigQuery and other Google Cloud Platform (GCP) services.
• Use Python and PySpark/Spark to transform, clean, aggregate and prepare data for analytics/ML.
• Orchestrate workflows using Cloud Composer (Airflow) to schedule, monitor and operationalise jobs.
• Optimize query performance, partitioning, clustering and cost in BigQuery.
• Work with structured, semi-structured and unstructured data, integrating multiple data sources.
• Collaborate with data scientists, analysts, business stakeholders to translate requirements into data solutions.
• Implement data governance, quality checks, pipeline monitoring, version control & CI/CD practices.

Required Skills / Qualifications:

• Strong hands-on experience with GCP services: BigQuery, Cloud Storage, DataProc/Dataproc, Dataflow, Pub/Sub, Cloud Composer. For example: designing pipelines, data ingestion, transformations. (Several roles explicitly list BigQuery + Composer + DataProc or Dataflow)
• Proficiency in Python (scripting, ETL, automation) and PySpark (or Spark) for large-scale data processing.
• Excellent SQL and BigQuery SQL skills, including query optimization, partitioning/clustering design.
• Experience with workflow orchestration tools: Cloud Composer (Airflow) or equivalent scheduling tools.
• Experience building and managing ELT/ETL/data-warehouse solutions at scale (data modelling, schemas, star/snowflake, analytics).
• Good understanding of cloud-native architecture, cost optimisation, data security, monitoring, and possibly DevOps/CI/CD.
• (Preferable) Certifications such as Google Cloud Professional Data Engineer or hands-on large-scale projects in GCP.