GCP Data Platform Engineer - Automation & Innovation Department

Join a new, strategic datatransformation project moving analytics from onpremise to GCP and building data architecture and data model from the ground up, focusing on business value creation and customer experience. The project utilizes technologies including GCP, Spark, Python, Kubernetes, BigQuery, Vertex AI, Terraform, and Looker. It integrates diverse, high-volume data sources, designs streaming and batch processing layers, and implements data governance, lineage, data quality, data security as well as CI/CD and monitoring/SLOs to support AI/LLM driven solutions.

• 3+ years of experience as a Data Platform Engineer in a data-driven environment, preferably with GCP.
• Experience developing enterprise-ready solutions based on GCP data services (BigQuery, Cloud Storage, Pub/Sub, Dataproc, Composer, Cloud Run, Looker, Vertex AI).
• Experience with 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 Infrastructure-as-Code (IaC) tools, including Terraform/Terragrunt.
• Proficiency in Python.
• Experience with Linux, Docker/Kubernetes, and GitLab CI/CD pipelines.
• Very good command of English (spoken and written).
• Strong communication skills to explain complex technical concepts to business stakeholders.

Nice to have:

• Degree in Computer Science.
• Experience with Spark.
• Knowledge of Scala or Java.
• Knowledge of 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.