GCP Data Platform Engineer - Automation & Innovation Department

Join a new, strategic data transformation project, moving analytics from on-premise to GCP and building data architecture and data model from the ground up, focusing on business value creation and customer experience. The project uses technologies like GCP, Spark, Python, Kubernetes, BigQuery, Vertex AI, Terraform, and Looker. It integrates diverse, high volume data sources, designs streaming and batch processing layers, implements data governance, lineage, data quality, and data security, and sets up CI/CD and monitoring/SLOs to shorten the path from question to answer and create a foundation for AI/LLM driven solutions.

• 3+ years of experience as a Data Engineer in a data‑driven environment.
• Experience in large‑scale data migration or cloud transformation projects.
• Experience with modern data platform patterns, including data lakehouse architectures on GCP (Cloud Storage + BigQuery).
• Hands‑on experience with GCP data services (BigQuery, Cloud Storage, Pub/Sub, Dataflow/Dataproc, Composer, Looker, Vertex AI).
• Hands‑on experience with Infrastructure‑as‑Code (IaC) tools, including Terraform.
• Strong SQL skills and experience with large‑scale data processing (Spark required; batch and streaming).
• Proficiency in Python and/or Scala or Java.
• Experience with Linux, Docker/Kubernetes and CI/CD pipelines.
• Very good command of English (spoken and written).
• Strong communication skills with the ability to explain complex technical concepts to business stakeholders.

Nice to have:

• Degree in Computer Science, Data Science or a related field.
• Experience with data governance, metadata and data quality tools.
• Experience collaborating with business stakeholders.

• Develop reusable frameworks for data processing and testing on GCP (e.g., BigQuery, Dataflow/Dataproc, Composer).
• Build and maintain batch and streaming data ingestion pipelines from various sources (databases, Kafka/MQ, APIs, files) into GCP.
• Implement automated tests and data quality checks for data pipelines.
• Collaborate with analysts and data scientists to deliver reliable, well‑documented datasets.
• Monitor, optimize and secure data pipelines in line with data governance and compliance standards.