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Remote mid-level data engineer role with popular skills but niche Microsoft Fabric reduces density.
Data engineering skills transferable, but Microsoft Fabric and Azure-specific expertise increases domain sensitivity.
Mandatory 4+ years plus specific Microsoft Fabric, Azure and PySpark skillset increases shortlisting strictness.
Own end-to-end data quality engineering tasks using Microsoft Fabric components including Lakehouse, Warehouse, Notebooks, and Pipelines.
Perform data validation, reconciliation, migration testing, and implement data quality frameworks focusing on checks like row count, nulls, duplicates, and source-to-target reconciliation.
Validate semantic models, KPIs, and Power BI reports while monitoring data quality metrics such as schema drift, data freshness, and anomaly detection.
Minimum 4 years of experience in Data Engineering or Data Quality Engineering.
Strong hands-on skills in Microsoft Fabric (Lakehouse, Warehouse, Notebooks, Pipelines).
Proficiency in SQL, Python, and PySpark.
Experience with Azure Data Factory, Power BI, Git, and Azure DevOps.
Experienced in implementing comprehensive data quality frameworks across large-scale data environments with Microsoft Fabric.
Skilled in deep data validation techniques including reconciliation and anomaly detection to ensure high data integrity.
Comfortable working remotely with off-hour shifts and immediate joining availability.