KHALID M.

• Design and development of modern Lakehouse architectures on Microsoft Fabric, following Bronze, Silver and Gold layers. • Development of scalable ETL/ELT pipelines using Microsoft Fabric, PySpark, Spark SQL and SQL. • Data modelling and preparation of analytics-ready datasets for Power BI and Direct Lake semantic models. • Design and development of interactive Power BI dashboards and KPIs for operational and strategic decision-making. • Development of complex SQL transformations, data quality controls, deduplication logic and business rules across large datasets. • Implementation of Dev / Pre-production / Production environments and deployment processes for Fabric data solutions. • Collaboration with business and technical teams to translate functional requirements into scalable data products. • Contribution to Machine Learning and AI projects, including data preparation, feature engineering, predictive modelling and evaluation using Python. • Exploration and development of Generative AI / RAG solutions, connecting AI agents with enterprise data to enable natural-language querying and intelligent data assistance. • Work on AI-enabled analytics and self-service BI, combining governed data models with generative AI capabilities to make enterprise data more accessible to end users.

Anti-Money Laundering (AML) Expertise: Extensive experience in building machine learning models for detecting suspicious financial activities to aid AML efforts. Data Analysis & Reporting: Use of NLP and statistical analysis to inform policy decisions and reduce false positives in financial anomaly detection by 20%. Cloud-Based Solutions: Developed and implemented cloud-integrated NLP models on AWS and Azure, improving data processing efficiency. Coordination & Compliance: Worked closely with national financial institutions and law enforcement agencies to ensure alignment with AML/CFT regulations.