About this role
Who we are
We are looking for a DataOps / Quality Framework Engineer to design, build, and maintain the technical framework that enables automated data quality controls, source freshness monitoring, pipeline alerting, completeness validation, and operational logging across our data platform ecosystem.
The role is focused on establishing and evolving scalable DataOps capabilities within Microsoft Fabric, ensuring data products are reliable, observable, and governed through automated quality and monitoring frameworks. The engineer will work closely with Data Engineering, Analytics, Architecture, Platform, DevOps, and business teams to implement robust controls that improve trust, transparency, and operational excellence across enterprise data pipelines.
The successful candidate will combine strong engineering expertise with a proactive approach to data quality, observability, automation, and operational monitoring, helping teams identify and resolve data issues before they impact downstream consumers.
What you'll be doing
- Analyze data platform requirements and design scalable frameworks for automated data quality and operational monitoring
- Build and maintain data quality controls, validation rules, completeness checks, and reconciliation processes across data pipelines
- Develop source freshness monitoring solutions to ensure timely and reliable data delivery
- Implement pipeline alerting, operational logging, exception handling, and incident notification mechanisms within Microsoft Fabric
- Design and develop reusable monitoring and observability components using SQL, Python, and PySpark
- Integrate quality frameworks with enterprise monitoring platforms, APIs, and notification services
- Create automated anomaly detection, threshold-based alerting, and data health scoring capabilities
- Establish monitoring standards, operational dashboards, and reporting for data reliability and platform performance
- Collaborate with Data Engineers and Platform teams to embed quality controls into data ingestion, transformation, and delivery processes
- Troubleshoot data quality incidents, perform root cause analysis, and drive continuous improvements to operational resilience
What you'll bring along
- BSc/MSc in Computer Science, Information Systems, Data Engineering, or a related field
- 6+ years of experience in Data Engineering, DataOps, Data Quality, or related data platform roles
- Strong hands-on experience with Microsoft Fabric, data pipelines, notebooks, and data integration services
- Advanced SQL skills and practical experience developing data validation and monitoring solutions
- Strong programming experience with Python and PySpark
- Experience implementing data observability, monitoring, alerting, and operational logging frameworks
- Knowledge of data quality concepts including completeness, accuracy, consistency, timeliness, reconciliation, and validation controls
- Experience integrating APIs, monitoring platforms, and enterprise notification services
- Familiarity with CI/CD practices, source control, automation, and DevOps principles for data platforms
- Strong analytical, problem-solving, and troubleshooting capabilities
- Excellent communication and stakeholder management skills
- Professional English proficiency
- Experience building scalable, automated frameworks that improve data reliability, governance, and operational excellence across enterprise environments