Summary
Maaden is a mining and metals company, and this role is based in Riyadh. The Lead Data Engineering Specialist builds and optimizes enterprise data pipelines, strengthens data quality and governance, and improves platform performance and reliability. The role also provides technical guidance and works with data, platform, governance, and business teams. Applicants should have a relevant bachelor's degree and 7–10 years of experience in data engineering, ETL/ELT development, and modern data platforms.
The posting
As published on maaden.com.sa. Pulled from the employer's site and shown as they wrote it.
Maaden, established in 1997, is one of the fastest-growing mining companies in the world and the largest multi-commodity mining and metals company in the Middle East. We are leading the development of the mining industry to become the third pillar of Saudi Arabia’s economy by building a world-class, unique, and fully integrated mining value chain.
We are pleased to share an exciting opportunity for the below mentioned position. This role offers a chance to contribute to our ambitious growth and play a key part in shaping the future of mining in the Kingdom.
Why This Role Matters
The Lead Specialist, Engineering and ETL II enables trusted, reliable, and accessible data across the enterprise by building and optimizing scalable data pipelines that transform raw data into high-quality information assets. The role ensures data products are delivered with strong governance, operational resilience, and performance standards that support business intelligence, analytics, and decision-making.
By embedding engineering excellence, data quality controls, and platform standards into data delivery processes, the role strengthens confidence in enterprise data, accelerates insight generation, and enables data-driven transformation initiatives. The position also provides technical leadership that improves engineering consistency, operational reliability, and long-term platform sustainability.
What You Will Deliver Data Engineering & Pipeline Delivery
- Deliver scalable ETL/ELT pipelines that provide reliable, governed, and high-quality data for analytics and reporting.
- Enable efficient ingestion, transformation, and delivery of data products aligned with approved data models and architecture standards.
- Maintain engineering practices that ensure consistency, reusability, and long-term maintainability of data solutions.
Data Quality & Governance
- Strengthen trust in enterprise data through robust validation, reconciliation, testing, and quality control mechanisms.
- Ensure metadata, lineage, and discoverability requirements are embedded across all data assets and pipelines.
- Enable governed access to curated datasets that support enterprise-wide data consumption.
Platform Performance & Reliability
- Optimize pipeline efficiency, scalability, and operational performance through continuous tuning and workload optimization.
- Improve production stability through monitoring, alerting, incident management, and proactive risk mitigation.
- Reduce operational disruptions by enhancing reliability, restartability, and recovery capabilities.
Technical Leadership & Engineering Standards
- Provide technical guidance that enhances engineering quality, consistency, and delivery excellence.
- Develop reusable components, standards, and engineering playbooks that improve productivity and reduce complexity.
- Influence technical decisions that strengthen enterprise data engineering capabilities.
Collaboration & Delivery Excellence
- Partner with data modelers, governance teams, platform specialists, and business stakeholders to deliver integrated data solutions.
- Ensure engineering outputs align with enterprise data definitions, governance requirements, and business needs.
- Drive continuous improvement initiatives that enhance cost efficiency, operability, and solution effectiveness.
What Success Looks Like
- Enterprise data pipelines consistently deliver trusted, accurate, and timely data products.
- Data quality issues are identified proactively and resolved efficiently with clear ownership and accountability.
- Pipeline performance and operational costs are continuously optimized without compromising reliability.
- Data assets are discoverable, governed, and supported by accurate metadata and lineage.
- Production incidents are reduced through strong engineering practices, monitoring, and operational excellence.
- Engineering standards and reusable assets improve delivery speed, quality, and scalability across initiatives.
Minimum Qualifications
- Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related discipline.
Experience
- 7–10 years of relevant experience in data engineering, ETL/ELT development, and modern data platforms.
- Experience delivering enterprise-scale production data pipelines with strong reliability and quality controls.
- Experience implementing data quality, governance, and operational monitoring practices.
- Experience providing technical leadership and collaborating across engineering, governance, and business teams.
Skills That Matter Functional Expertise
- ETL/ELT Engineering
- Data Pipeline Development & Optimization
- Data Transformation & Processing
- Data Quality Management
- Metadata & Data Lineage Management
- Cloud Data Platform Technologies
Business & Delivery
- Performance Optimization
- Data Governance & Compliance
- Operational Monitoring & Support
- Incident & Problem Management
- Testing & Automation
- Continuous Improvement
People & Collaboration
- Technical Leadership
- Stakeholder Management
- Cross-Functional Collaboration
- Vendor Coordination
- Communication & Documentation
- Ownership & Accountability
Benefits
1. Career Growth
2. Competitive Salaries
3. Comprehensive Package
4. Inclusive Work Environment
5. Work-Life Balance
6. Employee wellness