Malmo, Skåne län
Job Summary
The Data Engineer will design, build, and maintain scalable data pipelines, data models, and cloud-based infrastructure supporting Content Management capabilities. The role contributes to Content Insights, AI-enabled use cases, and DAM data layers, ensuring a sustainable data foundation aligned with business objectives and transformation goals.
Key Responsibilities
Data Engineering & Pipeline Development
Design, build, and maintain reliable data pipelines for CMS, DAM, and Content Insights platforms.
Develop ETL/ELT workflows to ingest, transform, and distribute data efficiently.
Ensure scalability, performance, and reliability of data processing systems.
Data Modelling & Architecture
Create and maintain data models supporting analytics and AI/ML use cases.
Structure datasets for Content Insights, DAM reporting, and AI pipelines.
Ensure consistency, reusability, and maintainability of data structures.
Cloud Platform (GCP)
Develop and manage data solutions on Google Cloud Platform (GCP).
Work with BigQuery and data pipeline tools to store and process large datasets.
Maintain performance and scalability of cloud data infrastructure.
Data for AI & Insights
Support data needs for content insights, analytics, and AI-enabled capabilities.
Enable datasets for image classification, ratings & reviews, and user-generated content analysis.
Collaborate with Data Scientists and MLEs to support machine learning workflows.
Data Quality & Governance
Ensure data quality, accuracy, and integrity across pipelines and systems.
Implement monitoring, validation, and governance practices.
Support sustainable and maintainable data architecture.
Cross-functional Collaboration
Collaborate with Product Owners, Engineers, Data teams, and stakeholders.
Support cross-functional initiatives across Growth & Marketing and related domains.
Contribute to Agile delivery cycles and team objectives.
Skill Requirements
Required Skills & Experience
Strong experience with Python and SQL.
Experience with BigQuery, dbt, and GCP.
Hands-on experience in data pipeline development and data modelling.
Understanding of ETL/ELT processes and data warehousing.
Experience in Agile and cross-functional environments.
Business-level English communication skills.
Preferred / Nice-to-Have Skills
Experience with AI/ML pipelines and data for analytics.
Knowledge of CMS/DAM ecosystems or marketing data platforms.
Experience with large-scale data systems and cloud architectures.
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