Senior Data Engineer at QS specializing in designing and building high‑quality data assets. Working in a hybrid model, effectively contributing to international higher education analytics.
Responsibilities
Design, build, and maintain high‑quality data assets by combining and harmonising third‑party datasets, proprietary QS data, scraped collections, and internal data sources
Develop, optimise, and maintain dbt models, tests, and documentation to ensure transparency, reproducibility, and engineering excellence
Partner closely with the Data Lead to ensure: comprehensive data lineage from raw ingestion through to final assets, robust source‑level and transformation‑level data quality checks, consistent metadata capture and governance
Apply advanced data modelling techniques to unify heterogeneous data into coherent, scalable structures that support ranking methodologies, research insights, and analytics
Profile and validate source datasets, conducting thorough statistical, structural, and semantic checks before asset integration
Build and maintain standardised transformation frameworks that improve trust, comparability, and traceability across QS’s data assets
Collaborate with the wider Data Engineering team to ensure alignment with platform standards, transformation principles, and shared engineering practices
Support data scientists, analysts, and BI teams by delivering well‑documented, dependable data assets that meet evolving analytical needs
Uphold rigorous data governance by embedding validation and documentation throughout workflows
Document data models, transformation logic, and operational procedures clearly and consistently
Mentor team members in dbt, modelling, asset development, and best practices in data quality and lineage
Requirements
Significant hands‑on experience designing, building, and maintaining dbt transformation pipelines
Proven capability in developing data assets that integrate heterogeneous sources including third‑party datasets, scraped data, and proprietary internal systems
Strong proficiency in SQL and Python, particularly for data shaping, ingestion orchestration, and quality automation
Practical experience working with cloud data platforms such as Snowflake, BigQuery, or equivalent
Deep understanding of data lineage, metadata management, governance, and reproducible modelling practices
Experience with data profiling, anomaly detection, schema validation, and quality assurance frameworks
Strong collaborative skills, especially in cross‑functional environments involving acquisition, sourcing, and analytics teams
Experience with data scraping pipelines or public‑data ecosystems
Familiarity with modelling datasets used for rankings, research, or analytical subscription products
Experience in higher education technology is appreciated but not required
Benefits
Competitive base salary
Access to an annual bonus scheme (for qualifying roles only)
20 days annual leave, plus public holidays
Generous maternity and paternity leave
Access to an Employee Assistance Programme (EAP) and MiCare health
A vibrant social environment and multicultural and multinational culture
Free subscription to the Calm App – the #1 app for sleep, meditation, and relaxation
Access to a variety of diversity and inclusion initiatives and groups
Strong recognition and reward programs – including a peer-to-peer recognition platform, quarterly and annual QS Applaud Awards, Connect with your Career annual PD event
Support for volunteering and study leave
Free subscription to LinkedIn learning – with over 5000 courses and programmes at your fingertips
Options to join our outstanding global Mentorship programme
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