Najla Albariqi

• Complex Data Querying and Optimization: Design and implement advanced SQL queries utilizing techniques such as Common Table Expressions (CTEs), window functions, and performance tuning (e.g., indexing and query execution plans) to ensure efficient data retrieval for analytical purposes.
• Power BI Data Modeling and Integration: Develop optimized SQL-based datasets and integrate them into Power BI for creating dynamic dashboards, incorporating calculated measures, DAX expressions, and relationship management to enhance data visualization and business insights.

Although my official title was Computer Engineer, my role spanned across Business Intelligence, development, and end-to-end ETL pipeline engineering. I operated as a hybrid resource bridging technical development with data analytics, transforming raw data into actionable insights.

My responsibilities included:

• Building, optimizing, and automating ETL pipelines to ensure data accuracy, reliability, and scalability.
• Designing and maintaining data models and integrating them with BI dashboards and reporting platforms.
• Gathering business requirements and converting them into technical, implementable data solutions.
• Supporting application development, maintaining data-related systems, and enhancing technical components.
• Improving data quality, streamlining data flows, and delivering data-driven solutions for operational teams.

This blend of engineering and BI allowed me to deliver full data solutions—from extraction and transformation, to analysis, visualization, and decision enablement.

• AI-Enhanced Data Analytics: Develop streamlined solutions combining SQL for data retrieval and Python for integrating lightweight machine learning models, enabling companies to extract predictive insights and automate decision-making processes.
• ML-Powered Data Visualization: Collaborate with businesses to design dynamic Power BI dashboards augmented with machine learning insights, using tools like Scikit-learn and TensorFlow to highlight trends, anomalies, and actionable metrics.

• Built end-to-end analytical workflows covering data ingestion, transformation, modeling, and insight delivery.
• Developed predictive and prescriptive models using Python, SQL feature engineering, and advanced time-series methods.
• Designed scalable BI data models and semantic layers that enhanced dashboard performance and analytical depth.
• Engineered automated ETL/ELT pipelines with orchestration, monitoring, and robust error-handling components.
• Created dynamic, drill-through Power BI dashboards using advanced DAX and optimized data modeling.
• Automated recurring analytical processes and enabled self-service analytics across business teams.
• Applied rigorous data validation, anomaly detection, and cleansing techniques to strengthen data quality.
• Collaborated closely with cross-functional teams to translate business needs into actionable analytical