Analytics Engineer (Data & Cloud)
Crystalloids - Google Cloud Partner · Netherlands ·
- Category
- Data analytics
- Experience
- 3+ years
- Company size
- 51-200
Crystalloids - Google Cloud Partner · Netherlands ·
· ROTTERDAM, Netherlands
· ROTTERDAM, Netherlands
Propel · Toronto, Ontario, Canada
simcorp · Bad Homburg
Build and maintain scalable data platforms on Google Cloud, transforming raw data into clean, documented datasets using dbt, SQL, Python, and GCP services like BigQuery and Cloud Composer.
Crystalloids was founded in 2006 and consists of qualified software and data specialists that help organizations innovate and grow their business by building data-driven solutions. We are a Premier Google Cloud partner. We believe that the foundation for a successful solution is based on three dimensions: the people, the processes and the technology. At Crystalloids we always take these three dimensions into account when we build solutions for our customers.
We have gained a very respectable client portfolio over the years. Our clients operate in market verticals like retail, e-commerce, CPG, media, travel, leisure such as Rituals, KNVB, Body & Fit, FD Mediagroep, ACSI.
Crystalloids empowers organizations to become truly data-driven by building scalable and efficient data platforms on Google Cloud. In your role as an Analytics Engineer, you’ll operate at the intersection of data engineering and business analytics. This means not only transforming raw data into meaningful insights using tools like dbt, Python, and SQL, but also contributing to core data engineering tasks within the Google Cloud Platform (GCP) ecosystem.
Are you passionate about creating well-modeled, tested, and documented datasets that drive business value? Do you enjoy collaborating with both technical and business stakeholders?
Design and implement robust data models using dbt to transform raw data into clean, documented, and reusable datasets for reporting and analytics.
Collaborate with stakeholders to understand business logic and requirements, and translate them into technical solutions that are traceable, testable, and version-controlled.
Write efficient and maintainable SQL, Python, or an object-oriented language for data transformation pipelines, ensuring performance and scalability on BigQuery and other GCP services.
Take ownership of data engineering tasks to ensure robust data ingestion and reliable pipeline execution using tools such as Cloud Composer, Cloud Run, Pub/Sub, and Cloud Storage. This includes building and maintaining data pipelines, not just collaborating with Data Engineers.
Implement "Analytics Engineering as Code" principles, including automated testing, CI/CD pipelines, and version control using Git and Terraform (where applicable).
Ensure all transformations and data sets are thoroughly documented to support transparency, reproducibility, and long-term maintainability.
db · Bucharest, 6A Dimitrie Pompeiu Blvd