∙ Consultant Big Data Engineer for a leading mobile telecommunications company in West Africa (MTN)

∙ Build and manage data pipelines leveraging Hadoop ecosystem technologies (Hive, Presto, HDFS) to process and orchestrate high-throughput data for over 90 million subscribers.

∙ Developed Sentinel, a Data Quality Framework that proactively detects and alerts on anomalies, reducing business escalations by ~40%.

Spearheaded an HDFS storage optimization initiative, conducting retention audits to identify, archive, or purge legacy tables, successfully reclaiming ~600TB of capacity.

∙ Participate in on-call rotations to proactively monitor business-critical pipelines, rapidly resolving operational bottlenecks to guarantee SLA compliance.

∙ Mentored aspiring data engineers through complex ETL concepts, adapting teaching methods to individual learning styles to ensure practical understanding and career readiness.

∙ Provided personalized feedback and career guidance to bridge the gap between theoretical study and professional application.

∙ Optimized and parallelized existing ETL workflows, reducing runtimes by up to 85%, improving downstream DAG efficiency, and accelerating access to critical business insights.

∙ Actively contributed to the Data Infrastructure Modernization project, which involved upgrading Apache Airflow and migrating existing data pipelines to a new staging and production framework, improving reliability, scalability, and maintainability.

∙ Participated in on-call rotations to proactively monitor infrastructure uptime and health, ensuring system reliability, rapid incident response, and minimal downtime for critical business operations.

∙ Spearheaded the data warehouse documentation, promoting data governance, enhancing accessibility, and ensuring compliance with organizational standards.

∙ Design lean data systems for growing SMEs, removing technical complexity to unlock clear visibility into core business metrics that directly accelerated leadership decision-making.

∙ Audit legacy data workflows to identify operational bottlenecks, advising stakeholders on cost-effective migration strategies and modern engineering patterns.

∙ Facilitate seamless client onboarding, delivering structured training programs that successfully transitioned cross-functional teams to being data-centric.

The pipeline generates an online transaction dataset via a Python script, stores it in S3, and catalogs metadata using AWS Glue. Users query the data with Athena or perform analytics in Redshift, with external tools like Power BI for further insights.

This Pipeline runs a python script that ingest data from rick&morty api, dump the response to S3, trigger AWS Lambda to transform dumped data and load to another S3 ~transformed, set up snowpipe to load the transformed data into snowflake, connect to PowerBI to create visualization.