I’m a junior data engineer with practical experience designing and delivering end-to-end data engineering solutions on Microsoft Azure. My work spans data ingestion, transformation, storage, and analytics, with a strong …
I’m a junior data engineer with practical experience designing and delivering end-to-end data engineering solutions on Microsoft Azure. My work spans data ingestion, transformation, storage, and analytics, with a strong focus on building scalable, reusable, and production-ready pipelines.
I have hands-on experience with Azure Data Factory for orchestrating parameterized pipelines, Databricks for data processing and transformation, ADLS for data storage, and Azure Synapse Analytics for analytics and warehousing. I’m comfortable working with SQL and Python, implementing incremental data ingestion, data validation, and error-handling logic to ensure data quality and reliability.
My background in business analysis strengthens my ability to understand requirements, translate business needs into technical solutions, and communicate effectively with both technical and non-technical stakeholders. I approach problems systematically, care about clean design and documentation, and treat projects with a production mindset rather than as one-off exercises.
I’m continuously improving my skills through structured projects and self-driven learning, with a clear goal of becoming a well-rounded, high-impact data engineer who builds systems that are robust, maintainable, and useful.
Built and maintained an Enterprise Data Catalog, boosting discoverability and governance compliance.
Optimized ETL pipelines and SQL scripts, reducing processing time by 40%.
Created and deployed Power BI dashboards, improving reporting efficiency across departments.
Developed a Finance Data Mart, cutting reporting time by 30% across multiple business units.
Automated workflows with Power Automate, reducing manual intervention.
Built and maintained an Enterprise Data Catalog, Optimized ETL pipelines and SQL scripts, Created and deployed Power BI dashboards, Developed a Finance Data Mart, Automated workflows with Power Automate
Developed enterprise data pipelines for transformation, loading, and reporting.
Leveraged SQL, Python, and Power BI to extract insights from large datasets.
Delivered 5 stakeholder presentations, driving adoption of data-driven decision-making.
Improved pipeline performance, reducing processing time by 20%.
Developed enterprise data pipelines for transformation, loading, and reporting, Leveraged SQL, Python, and Power BI, Delivered stakeholder presentations, Improved pipeline performance
Contributed to a team use-case project and executive-style presentation.
Gained exposure to Agile delivery and enterprise cloud concepts.
Shadowed a Data Engineer to observe development workflows.
Contributed to a team use-case project and executive-style presentation, Gained exposure to Agile delivery and enterprise cloud concepts, Shadowed a Data Engineer
Designed and implemented a production-grade, end-to-end data engineering pipeline on Azure using Azure Data Factory, Databricks, and Delta Lake to process Netflix data at scaleBuilt dynamic, parameterized ADF pipelines with data validation, error handling, and reusable components to support multiple ingestion scenariosImplemented Databricks Autoloader for incremental ingestion of Netflix source files, ensuring scalable, fault-tolerant, and efficient data processingDeveloped parameterized Databricks notebooks for transformation and enrichment of Netflix datasets, orchestrated using Databricks WorkflowsModeled data using Delta Lake and Delta Live Tables, applying the medallion architecture (Bronze, Silver, Gold) to improve data quality, lineage, and reliabilityOptimized the data layer for analytics and downstream consumption, enabling efficient querying and insights on Netflix content, ratings, and trends
نظام التوظيف التشغيلي المعتمد على الذكاء الاصطناعي أولاً، الذي يوحّد ويؤتمت دورة التوظيف بالكامل للمؤسسات الحديثة حول العالم.
د.جوب منطقة حرة ذ.م.م. 2026 © جميع الحقوق محفوظة
