Data Engineer SQL/dbt - 20420
Veritaz AB · Stockholm · 6 månader – upp till 12 månader · Deadline 2026-11-25
Veritaz AB is looking for an experienced **Data Engineer** for a consulting assignment focused on building and maintaining scalable, high-quality data flows within a modern data platform. Working in close collaboration with business stakeholders, development teams, source-system owners, and data architects, the role carries significant individual responsibility for technical delivery. Key responsibilities include developing and optimising data pipelines for ingestion, storage, and processing, implementing data models and transformations for a modern data warehouse environment, and optimising BigQuery workloads to ensure reliable data quality and availability for analytics and decision support. The position requires substantial hands-on data engineering experience, particularly with **Jinja SQL and dbt**, Terraform for infrastructure management, and the Google Cloud Platform ecosystem, including BigQuery, Cloud Functions, Dataflow, Cloud Run, and Cloud Build. Applicants must also have practical experience with GitHub, CI/CD practices, Kafka or an equivalent event-streaming platform, alongside a solid grasp of data warehousing, data modelling, and the Kimball methodology. **Fluency in English** is required, while proficiency in Swedish, Python, platform administration, IAM, and AI-assisted development tools such as GitHub Copilot or Claude is considered advantageous.
Veritaz is a leading IT staffing solutions provider in Sweden, committed to advancing individual careers and aiding employers in ensuring the perfect talent fit. With a proven track record of successful partnerships with top companies, we have rapidly grown our presence in the USA, Europe, and Sweden as a dependable and trusted resource within the IT industry.
Job Overview
Role Summary
An experienced Data Engineer is required for a consulting assignment focused on building robust, scalable, and high-quality data flows within a modern data platform. The role is responsible for developing and maintaining pipelines for data ingestion, storage, and processing, ensuring strong performance, data quality, and availability for analytics and decision support.
You will contribute to data modelling, transformation, code structure, documentation, and data architecture best practices. The assignment involves close collaboration with business stakeholders, development teams, source-system owners, and data and information architects, with significant individual responsibility for technical delivery.
Key Responsibilities
- Develop, maintain, and optimise data pipelines for data ingestion, storage, transformation, and processing.
- Ensure data platform performance, reliability, accessibility, and high data quality for analytical and decision-support use cases.
- Design and implement data models and transformations for a modern data warehouse environment.
- Optimise BigQuery workloads and data pipelines.
- Apply best practices for code structure, technical documentation, and data architecture.
- Collaborate with business representatives, engineering teams, source-system owners, and data and information architects.
- Contribute independently to technical decisions, structure, and delivery progress in an evolving environment.
Technical Stack & Requirements
- Strong experience with Jinja SQL and dbt.
- Strong experience with Terraform for infrastructure provisioning and management.
- Experience with Google Cloud Platform (GCP), including BigQuery, Cloud Functions, Dataflow, Cloud Run, and Cloud Build.
- Experience with GitHub and CI/CD practices.
- Experience with Kafka or an equivalent event-streaming platform.
- Good understanding of Data Warehousing, data modelling, and the Kimball methodology.
- Experience with BigQuery and data-pipeline optimisation.
- Experience with Python, platform administration, IAM, and AI-assisted development tools such as GitHub Copilot or Claude is advantageous.
Qualifications & Experience
The ideal candidate has substantial hands-on experience as a Data Engineer, particularly in SQL-based transformation workflows, dbt, Terraform, and GCP-based data platforms. Fluency in spoken and written English is required. Fluency in Swedish is advantageous. The candidate should be comfortable taking ownership in situations where requirements may be unclear, balancing technical detail with a broader architectural perspective, and collaborating effectively across technical and business stakeholders.