Ocholi Idakwo
Senior Data Engineer
Houston, US.About
Ocholi Idakwo is a Senior Data Engineer with 10 years of experience, predominantly in fintech, specializing in building robust, scalable big data systems. At Meta, he transformed data infrastructure, reducing deployment times by 83% and query latency from hours to minutes, while enhancing fraud detection and data quality for millions of users. His expertise spans AWS, Spark, and Python, with a proven track record of solving complex data challenges and driving significant business impact.
Work
Austin, TX, US
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Summary
Led the design, development, and optimization of Meta's core data infrastructure, enhancing real-time analytics, fraud detection, and CI/CD pipelines for global scale.
Highlights
Reduced data infrastructure deployment time by 83% (from 3 months to 2 weeks) using AWS CodePipeline and blue/green deployments, enabling rapid global feature launches with zero service disruptions.
Engineered a modern AWS-based S3 data lake, reducing query latency from hours to minutes and enabling real-time 360-degree user analytics across Meta's diverse platforms.
Developed a real-time fraud detection system processing over 600,000 interactions per second and 2 TB of daily data, significantly improving fraud detection capabilities.
Optimized daily ETL processing time by 50% (from 8 to 4 hours) while handling 500 million records, improving overall data processing efficiency for global operations.
Established a robust data quality monitoring system performing over 400 million daily checks, preventing discrepancies in 5 million user interactions through proactive alerting.
Dallas, TX, US
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Summary
Drove the development and implementation of advanced machine learning and big data solutions for diverse clients, including predictive maintenance, NLP, and fraud detection systems.
Highlights
Developed a predictive maintenance system for 10,000+ machines, reducing failure incidents by 40% and generating $1.5M in annual maintenance cost savings.
Engineered an NLP optimization pipeline for customer service, processing over 100 million transcripts to identify 25 unique complaint patterns, improving customer satisfaction.
Automated a time series forecasting system for retail inventory, achieving 2-5% accuracy across 4 million SKUs and 500 stores, saving $2M annually through optimized stock.
Designed and deployed a real-time anomaly detection system for financial transactions, scoring over 50 million transactions daily with <200 ms latency, preventing $5M in annual fraud losses.
Implemented a computer vision system for PCB quality control, processing 100,000 PCBs weekly with 95% accuracy, saving $500K annually by reducing faulty shipments.
Skills
Cloud Platforms
Amazon AWS, AWS CodePipeline, AWS CodeBuild, AWS Glue, AWS S3, AWS Redshift, AWS Kinesis, AWS Lambda, AWS QuickSight, AWS EMR, AWS Deequ, Amazon SNS, Azure Data Factory, Azure Kubernetes Service, Google Cloud Platform (GCP), Cloud Composer.
Big Data Technologies
Hadoop, HDFS, YARN, Apache Spark, PySpark, Airflow.
Machine Learning & AI
Machine Learning, Natural Language Processing (NLP), Computer Vision, TensorFlow, SageMaker, XGBoost, Isolation Forest, Random Forest, Prophet, SVM, BERT, spaCy, MLflow.
Programming Languages
Python.
Databases & Data Warehousing
SQL, Oracle, Parquet, JSON.
Streaming Technologies
Apache Kafka, Kinesis Data Streams, Kinesis Firehose.
Data Analysis & Visualization
Data Analysis, Data Visualization, Tableau, Grafana.
Version Control
Git.
Productivity Software
Microsoft Office, Microsoft Excel.
Containerization & Orchestration
Kubernetes.