Senior Data Engineer (GCP/BigQuery, dbt)

Project overview

Senior Data Engineer needed for a contract role in Stockholm within Retail & Sales Analytics. You will design, build and operate scalable data pipelines and analytical data models end-to-end on GCP, with BigQuery at the core. The work spans ingestion, transformation, modeling and quality assurance, with strong focus on reliability, testing/monitoring, CI/CD automation, and cost/performance optimization. The stack includes dbt, Terraform (IaC), GitHub Actions, Python and SQL, collaborating closely with data analysts to make trusted data available for reporting and decision support.

Responsibilities

• We are looking for a Senior Data Engineer to join our team within Retail & Sales Analytics.
• As a Data Engineer, you will design, develop and manage scalable and robust data solutions for large data sets. You will work throughout the entire chain, from ingestion and transformation to modeled and quality-assured data that can be used for analysis, reporting and decision support.
• Our data platform is on Google Cloud Platform (GCP) with BigQuery as a central component. We work with dbt, Terraform, GitHub Actions, Python and SQL, among other things.
• Design, develop and further develop data pipelines and data models.
• Ensure high data quality, reliability and traceability.
• Optimize solutions based on performance, scalability and cost.
• Automate testing, deployment, and monitoring to create stable production solutions.
• Contribute to architecture, technical choices and common development principles.
• Work closely with Data Analysts and other competencies in the team to make data easily accessible and useful for analysis and decision-making.
• Take independent technical responsibility from design and development to production launch and management.
• Solid practical experience with GCP and BigQuery
• Experience in data modeling for analytical use cases

Requirements

• Google Cloud Platform (GCP)
• BigQuery
• SQL (advanced)
• dbt
• Data modeling for analytics (dimensional/modeling for BI)
• CI/CD and automated deployments (e.g., GitHub Actions)
• Infrastructure as Code (Terraform preferred)
• Python
• Production-grade data engineering practices (data quality, testing, monitoring, reliability)
• Performance and cost optimization in data platforms
• End-to-end ownership from design to production

Project details

• Start: flexible
• Remote: onsite
• Location: Sweden, SE