Adunoluwa Ajiye

● Designed and deployed scalable ETL pipelines using AWS Glue, AIRFLOW, S3 and Lambda and also Digital Ocean (Droplets, Database) to facilitate seamless data ingestion, processing, and storage. ● Orchestrated real-time data pipelines with AWS Kinesis and Lambda to monitor campaign performance and user engagement metrics, reducing latency in reporting by 40%. ● Led a team in implementing a CDC pipeline from PostgreSQL to a data lake and from PostgreSQL to another PostgreSQL, enabling efficient data updates for business intelligence ● Worked with cross-functional teams to use enriched datasets, achieving a 35% improvement in marketing ROI for retail companies ● Implemented data warehouses using AWS Redshift, enabling rapid access to high-volume marketing and customer interaction datasets for advanced analytics. ● Orchestrated the transformation of raw data into actionable insights, creating dynamic dashboards with Metabase that empowered stakeholders to make data-driven decisions, optimize strategies, and identify growth opportunities. ● Optimised data ingestion pipelines with Airbyte(ELT), achieving a 30% reduction in data processing time and ensuring quality data for analytics and decision-making in the mart layer. ● Architected an AWS Step Function workflow that uses EMR and Spark to process massive data, resulting in a 30% reduction in processing costs and an over 50% improvement in job execution times.

- Build end to end data pipeline on azure services - Increase processing time on no central platform - Designing and implementing data integration workflows ensuring seamless data flow across systems and empowering Stakeholders with timely and accurate information - Developing innovative solutions to analyze large transactions tables, resulting to efficiency and accuracy on ROI demand - Design and build high performance and scalable pipelines to support data science projects following software engineering practices - Learnt an in built data engineering framework used for ETL processes - Prepare data and feature engineering to be used in machine learning models

- Implemented an automated data extraction and processing system reducing data processing time by 80%. - Configured and maintained access controls for various data sources and systems ensuring compliance with regulatory requirements and company policies - Providing custom dashboards for adhoc reporting using SQL, metabase.