Who We AreWe 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 DoingAnalyze data platform requirements and design scalable frameworks for automated data quality and operational monitoringBuild and maintain data quality controls, validation rules, completeness checks, and reconciliation processes across data pipelinesDevelop source freshness monitoring solutions to ensure timely and reliable data deliveryImplement pipeline alerting, operational logging, exception handling, and incident notification mechanisms within Microsoft FabricDesign and develop reusable monitoring and observability components using SQL, Python, and PySparkIntegrate quality frameworks with enterprise monitoring platforms, APIs, and notification servicesCreate automated anomaly detection, threshold-based alerting, and data health scoring capabilitiesEstablish monitoring standards, operational dashboards, and reporting for data reliability and platform performanceCollaborate with Data Engineers and Platform teams to embed quality controls into data ingestion, transformation, and delivery processesTroubleshoot data quality incidents, perform root cause analysis, and drive continuous improvements to operational resilienceWhat You'll Bring AlongBSc/MSc in Computer Science, Information Systems, Data Engineering, or a related field6+ years of experience in Data Engineering, DataOps, Data Quality, or related data platform rolesStrong hands-on experience with Microsoft Fabric, data pipelines, notebooks, and data integration servicesAdvanced SQL skills and practical experience developing data validation and monitoring solutionsStrong programming experience with Python and PySparkExperience implementing data observability, monitoring, alerting, and operational logging frameworksKnowledge of data quality concepts including completeness, accuracy, consistency, timeliness, reconciliation, and validation controlsExperience integrating APIs, monitoring platforms, and enterprise notification servicesFamiliarity with CI/CD practices, source control, automation, and DevOps principles for data platformsStrong analytical, problem-solving, and troubleshooting capabilitiesExcellent communication and stakeholder management skillsProfessional English proficiencyExperience building scalable, automated frameworks that improve data reliability, governance, and operational excellence across enterprise environments Show more Show less