Hybrid Senior Data Engineer – 12-month contract

at QS Quacquarelli Symonds

• Job title: Senior Data Engineer – 12-month contract
• Job type: Full Time
• Experience level: Senior
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: BigQuery, Cloud, Python, SQL
• Location requirements: Hybrid, Kuala Lumpur, Malaysia

About the role

• 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