Godwin Agwu Ibiam

- Architected scalable data pipelines on AWS (S3, Redshift, Glue), processing millions of customer data points, reducing data processing time by 40% and improving operational efficiency. - Developed real-time data streaming systems using Apache Kafka and PySpark, improving business forecasting and reducing processing time by 15%. - Implemented automated data orchestration using Apache Airflow, optimizing ETL workflows, reducing deployment times by 35%, and automating 90% of data pipeline tasks. - Optimized data models and performed performance tuning using Snowflake and AWS Redshift, increasing data accessibility and query speed by 50%. - Improved data security and ensured GDPR compliance by implementing data encryption, role-based access control (RBAC), and audit logs for sensitive customer data. - Built structured data models and integrated datasets, enhance reporting accuracy and improve downstream analytics performance by 30%. - Developed interactive Power BI dashboards and reporting frameworks, enabling real-time insights and improving stakeholder decision-making efficiency by 25%. - Executed advanced data cleansing, validation, and transformation in Python, reducing data inconsistencies and improving data quality by 30%. - Collaborated with cross-functional stakeholders to define data requirements and translate them into scalable technical solutions, achieving 95% requirement alignment and delivery success.

- Architected and optimized an enterprise-scale data platform for satellite and RF networks (C, Ku, Ka bands), designing batch and streaming data pipelines processing terabyte-scale telemetry datasets, improving network performance and service quality by 20%. - Engineered resilient data integration systems combining OSS/BSS, RF, and network performance datasets, enabling 99.99% platform uptime and strengthening reliability of enterprise analytics and monitoring systems. - Developed high-performance data pipelines supporting adaptive coding, modulation, and dynamic power control analytics, improving bandwidth efficiency and reducing operational costs by 15%. - Integrated multi-source telecom datasets (structured and semi-structured) to enable real-time KPI and SLA monitoring, improving network stability and customer satisfaction by 25%. - Built scalable data solutions for transponder capacity planning and VSAT expansion, integrating vendor and legacy datasets into modern data platforms, improving spectrum utilization by 18% and reducing infrastructure costs. - Introduced automated data validation tools, improving the accuracy of data processed by 50%, reducing reporting errors and improving data integrity. - Collaborated with NOC, Service Delivery, Finance, and Engineering teams to unify data sources into a single analytical layer, improving operational visibility and decision-making speed across enterprise systems.