Backend Engineer Catalog, /dev/null - 20567
- Ort saknas i annonsen
- Tidsbegränsad anställning
- Heltid
- 6 månader – upp till 12 månader
- Distans
Sista ansökningsdag 2 december 2026
Veritaz is a leading IT staffing solutions provider in Sweden, committed to advancing individual careers and aiding employers in ensuring the perfect talent fit.
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Snabb överblick
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- Arbetssätt
- Distansarbete
- Anställning
- Tidsbegränsad anställning
- Omfattning
- Heltid
- Varaktighet
- 6 månader – upp till 12 månader
- Erfarenhet
- Erfarenhet krävs
- Språk
- Svenska krävs
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Om jobbet
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
Our client is seeking an experienced Backend Engineer for a consulting assignment focused on large-scale backend services, data pipelines, and storage within a complex cloud-based platform. The role involves systems that process and manage substantial data volumes, with high requirements for scalability, reliability, and traceability.
You will develop and enhance backend services, APIs, and data solutions while contributing to data modeling, lifecycle management, efficient storage, and the reuse of previously processed data. The assignment also covers secure data storage, access control, and technical solutions for environments subject to data retention, deletion, audit trail, and regulatory requirements.
Key Responsibilities
- Design, develop, operate, and improve scalable backend services and APIs in large production environments.
- Build and maintain data pipelines supporting both batch and real-time processing.
- Develop data models that support traceability, versioning, efficient querying, and long-term lifecycle management.
- Design APIs and schemas with a strong focus on backward compatibility and maintainability.
- Improve storage efficiency and enable the reuse of previously processed data across the platform.
- Integrate cloud-based data services such as object storage, NoSQL databases, messaging systems, and data warehouses.
- Implement infrastructure through code and contribute to reliable cloud deployment and operational practices.
- Develop solutions for encryption, key management, secure storage, and access control.
- Collaborate across multiple teams and clearly communicate technical decisions to both technical and non-technical stakeholders.
- Independently identify and prioritize engineering initiatives that provide the greatest technical and operational impact.
Technical Stack & Requirements
- Backend development: Extensive experience developing and operating backend services and APIs in large-scale production environments.
- Programming: Advanced proficiency in Java and/or Scala.
- Data processing: Practical experience with Apache Beam, Google Cloud Dataflow, or equivalent distributed data-processing technologies.
- Pipeline architecture: Experience designing and operating both batch and real-time data pipelines.
- Data architecture: Strong knowledge of data modeling, schema evolution, version management, traceability, and efficient search patterns.
- Cloud data services: Experience with object storage, NoSQL databases, message queues, and data warehouses.
- Infrastructure: Experience applying Infrastructure as Code practices.
- Security: Good understanding of encryption, key management, access control, and secure data storage.
Qualifications & Experience
The successful consultant will have substantial experience building and operating data-intensive backend platforms in production. Experience working in compliance-sensitive environments is required or strongly relevant, particularly where systems must support data retention, deletion, access governance, and auditable processing.
Experience with media, content, or other data-heavy platforms is advantageous, as is experience optimizing storage solutions for exceptionally large datasets. The role also requires the ability to become productive quickly in unfamiliar codebases and technical domains, contribute effectively when the full context is not yet known, and communicate engineering decisions clearly across team boundaries.
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