Data Engineer - 20180
Veritaz AB · Stockholm · 6 månader – upp till 12 månader · Deadline 2026-11-20
Veritaz AB is seeking an experienced senior **data engineer** for a consulting assignment focused on building and refining scalable data solutions for analytics, reporting, and decision support. The position covers the end-to-end data lifecycle, from ingestion and transformation to modelling, quality assurance, and production operations. Key responsibilities include creating production-ready pipelines and analytical models using Python and SQL, managing cloud resources via **Infrastructure as Code**, setting up automated CI/CD workflows, and collaborating with analytical and technical stakeholders. Daily tasks also involve optimising platform performance and costs while handling monitoring, testing, and system observability. Candidates must have substantial hands-on experience with **GCP and BigQuery**, strong SQL capabilities, and solid proficiency in dbt for analytical data modelling. Requirements also include proven experience developing production data solutions in Python, managing deployments through CI/CD pipelines, and applying Infrastructure as Code practices, preferably using Terraform. A thorough grasp of data quality, monitoring, and operational reliability is essential. Familiarity with Node.js, TypeScript, Kafka, Google Pub/Sub, REST APIs, Looker, LookML, and large, complex data platforms 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 Senior Data Engineer is needed for a consulting assignment focused on developing and enhancing scalable data solutions for analytics, reporting, and decision support.
Key Responsibilities
- Design, develop, deploy, and maintain robust data pipelines and analytical data models.
- Work across the complete data flow, including ingestion, transformation, modelling, data quality assurance, and production operations.
- Ensure data solutions meet requirements for performance, scalability, cost efficiency, and operational reliability.
- Develop production-ready data solutions using Python and SQL.
- Implement automated deployments and CI/CD workflows.
- Use Infrastructure as Code practices to provision and manage platform components.
- Collaborate with analytical and technical stakeholders on technical decisions, solution design, and delivery.
- Optimise data solutions for query performance, platform cost, and maintainability.
- Contribute to testing, monitoring, data quality, observability, and reliable operation of the data platform.
Technical Stack & Requirements
- Cloud platform: Google Cloud Platform (GCP) and BigQuery.
- Data transformation and modelling: dbt, SQL, and analytical data modelling.
- Programming: Python; Node.js and TypeScript are advantageous.
- Infrastructure and delivery: Terraform, Infrastructure as Code, GitHub Actions, CI/CD, and automated deployments.
- Integration and streaming: Kafka, Google Pub/Sub, and REST APIs are advantageous.
- Analytics and visualisation: Looker and LookML are advantageous.
- Operational capabilities: data quality, testing, monitoring, metadata, data lineage, and observability.
Qualifications & Experience
You have substantial hands-on experience with GCP and BigQuery, very strong SQL skills, and solid experience using dbt. You have worked with data modelling for analytical use cases and have experience implementing CI/CD pipelines and automated deployments. Experience with Infrastructure as Code, preferably Terraform, and Python-based production data solutions is required.
You should have a strong understanding of data quality, testing, monitoring, and operational reliability, as well as experience optimising data solutions for performance and cost. Experience working with large and complex data platforms is advantageous. The role requires the ability to work independently and systematically, analyse complex technical challenges, and take solutions through to production.