The work focuses on moving the data warehouse load layer into shared cloud-based ingestion and on simplifying and speeding up the load chains. The main goal for the client is to have a leaner, faster and more stable data warehouse. A near real-time sales data model will also be built as the foundation for a conversational BI solution.
Your responsibilities:
- Move the load layer into shared GCP ingestion projects and build reusable ingestion patterns (Datastream, Dataflow), project structure, IAM and Terraform modules.
- Simplify the data warehouse load chains, restructure Composer/Airflow DAGs and optimise BigQuery queries for runtime and cost.
- Build a near real-time sales data model on BigQuery and make it available in Looker.
- Define and track metrics for load duration, throughput, stability and cost.
- Ensure testing and documentation, and hand over patterns and practices to internal teams.
Requirements:
- Hands-on experience with GCP data services: BigQuery, Cloud Composer (Airflow), Dataflow and Datastream.
- Strong experience in data warehouse modelling and data warehouse principles.
- Python and SQL, with experience building production-grade data pipelines and tested, maintainable code.
- Terraform, CI/CD and git, and use of AI coding assistants in your own work.
- Fluent English communication with both business and technical experts, and a proactive, solution-oriented approach.
Nice to have:
- Experience with reusable ingestion patterns, CDC and streaming sources.
- GCP project structure, IAM and infrastructure as code at platform level.
- BigQuery performance and cost optimisation, and Airflow DAG design at scale.
- Incremental and near real-time data modelling.
- Looker / LookML and Finnish language skills.
Interested?
Apply with your CV via out backstage by clicking "interested". Due to the high number of applicants, applications sent via email will not be considered.