About this role
Who we are
We are looking for a hands-on DataOps and Data Quality Engineer who can combine Microsoft Fabric engineering with automated data quality, monitoring and operational reliability. The role is primarily technical and focuses on turning governance and quality requirements into automated controls, observability and robust platform processes while remaining flexible enough to support broader data engineering activities when required.
What you'll be doing
- Design and implement automated data quality controls within Microsoft Fabric to ensure reliable and trustworthy reporting data
- Translate data quality and governance requirements into executable validation rules, monitoring checks and quality gates
- Build automated checks for data freshness, completeness, duplicates, schema changes, data volumes, key-field validity and other agreed business rules
- Develop centralized monitoring and logging for pipeline runs, source arrival times, row counts, errors and data quality results
- Create alerting and operational views to identify missing data, abnormal volume drops, failed loads and broken dependencies before they impact KPI reporting
- Define reusable quality gate patterns and automated actions such as warnings, quarantine, reprocessing and escalation
- Support and improve Microsoft Fabric pipelines, Dataflows Gen2, notebooks and SQL/Python-based processes
- Implement robust pipeline patterns including retries, incremental loads, backfills, error handling and controlled reprocessing
- Support ingestion from APIs, files, ADX/Kusto and other enterprise data sources when required
- Embed data quality controls across ingestion, transformation and curated-data layers rather than relying on manual validation
- Apply configuration-driven engineering patterns, environment separation, Git/CI/CD and deployment best practices
- Troubleshoot data and pipeline incidents, perform root cause analysis and support controlled recovery
- Document thresholds, dependencies, data contracts, runbooks and recovery procedures
- Collaborate closely with Data Governance, Data Architecture, Data Engineering, Semantic Model and BI teams to improve overall platform reliability
What you'll bring along
- 3+ years of professional experience in Data Engineering, DataOps, Data Quality Automation, Analytics Engineering or a related back-end data role
- Hands-on experience with Microsoft Fabric or Azure data platforms
- Strong practical experience with Microsoft Fabric components such as Pipelines, Dataflows Gen2, Lakehouse/Warehouse, notebooks and SQL-based processing
- Experience implementing automated data quality checks, monitoring, logging, alerting or operational controls around production data pipelines
- Strong SQL skills and working knowledge of Python and/or PySpark for validation, automation and data transformation
- Experience supporting API, file, database or enterprise-source ingestion
- Good understanding of error handling, incremental loads, retries, backfills and controlled reprocessing
- Strong troubleshooting and root cause analysis skills
- Structured approach to documentation and operational procedures
- Ability to work effectively across Governance, Architecture, Data Engineering and BI teams
- Good English communication skills
- Nice to have
- Experience integrating ADX/Kusto with Microsoft Fabric or Power BI
- Experience with Azure Data Factory, Azure Functions, Eventstream, CDC or mirroring patterns
- Experience with data testing or observability frameworks such as dbt tests, Great Expectations, Soda or similar tools
- Experience with Git, CI/CD, environment promotion and automated deployment practices for data assets
- Knowledge of Microsoft Purview, lineage, metadata management, sensitivity labels or enterprise governance tooling
- Experience working in production reporting environments with strong operational and reliability requirements