Sibaliso M.

Architected ETL pipelines utilizing Python and DBT for transforming raw data into datasets ready for analysis, which enhanced business intelligence reporting in real-time. Deployed scalable data infrastructure on Google Cloud Platform, employing BigQuery for data warehousing, Cloud Functions for serverless operations, and Cloud Storage for efficient data lake management. Built data streaming solutions using Google Cloud Pub/Sub to facilitate event-driven architectures, processing significant event volumes. Developed RESTful APIs for data access, fostering communication between microservices and third-party systems. Implemented a data quality monitoring framework, ensuring the integrity and reliability of production pipelines. Collaborated with product managers and data analysts to convert business requirements into data solutions. Optimized database queries and data models to improve performance in dashboard loads and analytical queries. Used Firebase and Firestore for real-time databases in applications, thereby ensuring low-latency data access.

Acquired practical experience in machine learning and data science, successfully moving from physics research to the tech industry. Developed and trained machine learning models for business purposes, utilizing statistical analysis and predictive modeling techniques. Collaborated with senior data scientists on experiment designs, model performance evaluations, and algorithm optimizations. Employed Python and data science libraries to conduct exploratory data analysis and perform feature engineering. Engaged in agile development methodologies and participated in code reviews, integrating industry best practices in collaborative software development.

Instructed Grade 12 Physics and Mathematics, creating and delivering lesson plans aligned with national curriculum requirements. Simplified complex concepts in mathematics and physics to promote critical thinking and problem-solving abilities. Prepared students for matriculation exams through focused instruction and assessment methods. Honed communication and presentation skills by effectively conveying technical ideas to accommodate various learning styles. Demonstrated aptitude for distilling complex information, facilitating collaboration with non-technical stakeholders in data engineering.