Data Engineer - Deb
Role details
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Tech stack
Job description
We are seeking a hands-on Data Engineer or Associate Data Engineer with strong programming skills in Python and SQL and practical experience building scalable data ingestion, transformation, and quality pipelines using Azure ecosystem. This role will focus on building scalable ingestion pipelines into the Bronze layer, developing Silver and Gold layer patterns, with strong focus on data quality, and enabling governed semantic models through Microsoft Fabric for analytics and reporting. Technical Skills: Python, SQL, PySpark, Pandas, Azure Databricks, Azure Data Factory, Snowflake, Star Schema, Dimensional Modeling and Microsoft Fabric., Implement medallion lakehouse architecture patterns across Bronze, Silver, and Gold layers. Build and maintain ingestion pipelines that bring structured, semi-structured data, files, API sources into the Bronze layer of the lakehouse. Develop Silver layer patterns focused on data cleaning, standardization, deduplication, validation, and business-rule application. Develop Gold layer datasets that are curated, analytics-ready, and suitable for enterprise reporting and semantic model consumption. Build reliable data pipelines using Azure Databricks, SQL, and Python. Design and support orchestration workflows using Azure Data Factory Implement data quality checks, reconciliation logic, exception handling, and monitoring across pipelines. Support enterprise data warehouse design, including dimensional modeling, star schemas, fact tables, and dimension tables. Prepare curated datasets that support semantic models, governed reporting, dashboards, and business intelligence use cases. Enable semantic layers and semantic models through Microsoft Fabric, including Fabric Lakehouse, Fabric Warehouse, OneLake, and Power BI semantic model integration. Optimize data pipelines, transformations, models, and processing jobs for performance, scalability, and maintainability. Collaborate with data analysts, BI developers, business stakeholders, and platform teams to translate requirements into trusted, reusable data products. Communicate technical concepts clearly to both technical and non-technical stakeholders.
Requirements
Data Engineer 3-5 years of industry experience in data engineering, analytics engineering and enterprise data platforms Strong hands-on experience with SQL and Python for data pipeline development, transformation, validation, and performance optimization. Experience building ingestion pipelines into a lakehouse, cloud data platform, or enterprise data warehouse. Experience designing and implementing Bronze, Silver, and Gold layers in a medallion lakehouse architecture. Hands-on experience with Azure Databricks and distributed data processing. Experience with Snowflake, including data loading, transformation, performance tuning, and integration with cloud platforms. Experience implementing data cleaning, standardization, deduplication, validation, reconciliation, and data quality rules. Working experience with enterprise data warehouses, dimensional modeling, star schemas, fact tables, and dimension tables. Hands on experience with Microsoft Fabric a plus Experience creating curated datasets for reporting, analytics, dashboards, and semantic model consumption. Understanding of data governance, metadata management, lineage, monitoring, and production support best practices. Ability to own specific data domains, act as a subject matter expert, and guide implementation patterns. Preferred Qualifications Azure, Microsoft Fabric, Databricks, or Snowflake certification preferred. Experience with Delta Lake, Unity Catalog, OneLake, or similar lakehouse governance technologies. Experience implementing reusable ingestion, Silver transformation, and Gold curation patterns. Experience supporting Power BI or Microsoft Fabric semantic model development. Experience building production-grade data pipelines for enterprise analytics, reporting, or data platform modernization.
Benefits & conditions
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