Hamza Bakh

Consulting on Business Intelligence and Data Engineering initiatives, delivering reliable, scalable analytics solutions that support financial and operational decision-making. Key Responsibilities ▪ Design and maintain analytical data pipelines (ETL/ELT), applying data engineering best practices in data quality, performance, and governance ▪ Model analytical datasets (fact/dimension models) to ensure consistency and usability across BI and reporting layers ▪ Develop and deploy Power BI dashboards for financial and operational performance monitoring ▪ Optimize SQL queries and data structures to improve pipeline efficiency and KPI reliability ▪ Automate reporting workflows (Excel / VBA) to reduce manual effort and production time ▪ Document data models, pipelines, and business indicators to ensure maintainability and knowledge transfer ▪ Present insights clearly to stakeholders, bridging technical implementation and business interpretation.

Design & Deployment of an Embedded Business Intelligence Solution for a Secure Multi-Tenant SaaS Application Objective: Deliver a secure, scalable, high-performance BI platform for multiple SaaS clients. Key Achievements: ▪ Benchmarked Apache Airflow, Prefect, and Azure Data Factory for orchestration; selected Prefect for flexibility and hybrid deployment support ▪ Compared Snowflake, PostgreSQL, and SQL Server for warehousing; deployed Snowflake for cloud scalability ▪ Built modular SQL models with dbt for transformations and documentation ▪ Integrated Grafana dashboards for monitoring and freshness alerts ▪ Embedded Metabase with row-level security for custom KPI dashboards per tenant ▪ Implemented access control, encryption, and audit logging for compliance Impact: • Reduced dashboard delivery time by 35% • Increased system reliability through Grafana monitoring • Delivered 25+ tenant-specific dashboards to 100+ users Technologies: Snowflake, Prefect, Apache Airflow, SQL Server, PostgreSQL, Azure, dbt, Grafana, Metabase Embedded, GitLab CI/CD, Python, SQL, Docker

Project: Automated Data Extraction, Transformation & Reporting for Agricultural Analytics Objective: Automate the process of collecting, cleaning, and visualizing agricultural survey data to improve accuracy and reduce manual workload. Key Achievements: ▪ Automated ingestion of survey data from Excel and OSR files into SQL Server tables ▪ Developed Python ETL scripts (Pandas, NumPy) for data cleaning, transformation, and validation ▪ Benchmarked visualization tools (Power BI ) for interactivity, scalability, and ease of integration ▪ Selected and deployed Power BI for advanced analytics and interactive dashboards ▪ Designed dynamic dashboards enabling real-time agricultural performance analysis for clients Impact: • Reduced manual reporting workload by 40% • Improved decision-making speed through automated, real-time dashboards • Increased data accuracy by automating validation processes Technologies (used & benchmarked): SQL Server, Python, Pandas, NumPy, Power BI

Role: Data Scientist Intern Field: Machine Learning Model Development Scope: Data Analysis and Modeling Project: Development of machine learning models for various datasets and use cases

Developed a complete Graduate Tracking Management System , enabling the digitalization of lauréat monitoring and administrative workflows. Built a desktop application using VB6 connected to an SQL database, automating data entry, reporting, and archival operations. Key contributions: • Designed and implemented user interfaces, business logic, and data workflows using Visual Basic 6. • Built a structured SQL database to manage student records, promotions, internship details, and insertion data. • Developed automated Crystal Reports dashboards for performance statistics, cohort follow-up, and administrative reporting. • Streamlined multiple manual Excel-based processes by integrating automated data extraction and validation. • Improved data accuracy and consistency through optimized SQL queries and validation rules. • Collaborated with the administrative team to define functional requirements and testing scenarios. • Delivered training sessions and documentation to ensure proper adoption of the system. • Contributed to the digital transformation of internal processes, reducing manual workload and increasing data reliability. This project strengthened my foundation in software development, database management, reporting, and the design of operational information systems.