Temiloluwa A is a Data Engineer with over 4 years of experience in data warehousing, pipeline development, and ETL optimization. He works with Python, SQL, Apache Spark, Apache Doris, Apache Kafka, and Apache Airflow, building CDC pipelines, data warehouses, and orchestrated ETL workflows on AWS services including Glue, S3, EMR, and Lambda. His work includes real-time data streaming, workflow orchestration, and query performance tuning for business-critical reporting. At Everlytics Data Science, he engineered a CDC pipeline moving data from Oracle, MS SQL Server, and PostgreSQL into Apache Doris, reducing data latency from hours to under 40 seconds. He led a data warehouse migration from Teradata to SingleStore that cut warehousing costs by 40% and improved query performance by 25% through database enhancements. Additionally, he optimized SQL queries to increase execution speed by 40% and reduced release cycles by 20% through workflow dependency management. He holds a B.Tech in Physics Electronics and is 2x AWS Certified. Temiloluwa is well suited for data warehouse migration, ETL pipeline, and real-time data streaming engagements.
Ingest Spotify listens data from Spotify's API, and ingest data to an AWS RDS Postgres Database. Airflow is used for orchestration and is deployed on EC2 compute.
A Serverless ETL Pipelines to transform data on S3 PUT and load to Staging area. It is built for scalability with SQS and SNS to manage queues.
An API that predicts the nature of toxicity of a sentence with multi-label prediction. The nature of toxicity that can be detected are: Toxic, Severe Toxic, Obscene, Threat, Insult, Identity Hate. The API can return more than a single label (multi-labeled) and the word Not-Toxic if the text isn't toxic.
This project is a webapp and API that anonymizes naming entities in text.
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Data Engineer
Lagos, Nigeria · 3-4 years experience
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