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Cyrile Nnoko Mbebi portrait

Data analytics from clear questions to clear answers

I turn raw data into concise insights and visualizations that support decisions and tell the story behind the numbers.

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Work samples

Projects that show data cleaning, statistical analysis, and visual reporting from question to conclusion, using practical tools and methods learned in my Data Science degree.

  • Time-Series Inventory Replenishment Pipeline (Capstone)
    Time-Series Inventory Replenishment Pipeline (Capstone)

    Designed and implemented an end-to-end inventory pipeline to automate ETL workflows, predict stockout events, and improve supply chain decisions using Python, SQL, SQLite, Apache Airflow, Pandas, Scikit-learn, Tableau, Git Built ETL workflows to ingest, clean, validate, and transform 10,000+ inventory records. Engineered time-series features and developed Logistic Regression and Random Forest models. Designed relational database schema and Tableau dashboards. Achieved 75%+ prediction accuracy. Automated reporting and enabled proactive replenishment planning.

    PythonSQLSQLiteApache AirflowPandasScikit-learnTableauGit
  • AWS Airflow Data Pipeline
    AWS Airflow Data Pipeline

    Built a cloud ETL pipeline to automate ingestion and loading of transactional data using Apache Airflow, AWS S3, Amazon Redshift, PostgreSQL, Python, SQL. Developed Airflow DAGs, loaded data from S3 into Redshift, and Implemented SQL transformations and data quality validation. Processed 50K+ records, reduced manual processing by 60%, and detected 95% of data inconsistencies.

    Apache AirflowAWS S3Amazon RedshiftPostgreSQLPythonSQL

About me

I turn raw data into concise insights and visualizations that support decisions and tell the story behind the numbers.

I am Cyrile Nnoko Mbebi, a data analytics professional and Data Engineering graduate from Western Governors University. I specialize in Building ETL Pipelines , and transforming data into clear insights and actionable solutions for decision-making. My work involves ETL Pipelines, analyzing datasets and using visualization techniques to clarify what the numbers reveal.

Externships

SQL & Database Architecture Externship with Breaking Games

Breaking Games

Experience

Data Analytics

Springboard · Apr 2022 - Jul 2026

Education

Western Governors University (WGU)

Master of Science, Data Analytics 6 Data Engineering · Class of 2026

Udacity

Nanodegree: Data Engineering with AWS · Class of 2025

Udacity

Nanodegree: Data Engineering with Microsoft Azure · Class of 2025

Skills

Data cleaningStatistical analysisData visualizationExploratory data analysisReport writingETL PipelineData StorytellingData mining
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Time-Series Inventory Replenishment Pipeline (Capstone)

Designed and implemented an end-to-end inventory pipeline to automate ETL workflows, predict stockout events, and improve supply chain decisions using Python, SQL, SQLite, Apache Airflow, Pandas, Scikit-learn, Tableau, Git Built ETL workflows to ingest, clean, validate, and transform 10,000+ inventory records. Engineered time-series features and developed Logistic Regression and Random Forest models. Designed relational database schema and Tableau dashboards. Achieved 75%+ prediction accuracy. Automated reporting and enabled proactive replenishment planning.

PythonSQLSQLiteApache AirflowPandasScikit-learnTableauGit
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AWS Airflow Data Pipeline

Built a cloud ETL pipeline to automate ingestion and loading of transactional data using Apache Airflow, AWS S3, Amazon Redshift, PostgreSQL, Python, SQL. Developed Airflow DAGs, loaded data from S3 into Redshift, and Implemented SQL transformations and data quality validation. Processed 50K+ records, reduced manual processing by 60%, and detected 95% of data inconsistencies.

Apache AirflowAWS S3Amazon RedshiftPostgreSQLPythonSQL
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AWS Data Lake Pipeline

Designed a scalable AWS data lake for semi-structured data using AWS S3, AWS Glue, Amazon Athena, Python, SQL. Designed Raw, Trusted, and Curated layers, Built Glue ETL jobs, and Created Athena external tables. Processed 100K+ JSON records, Reduced transformation time by 50%, and Improved query performance by 30%.

AWS S3AWS GlueAmazon AthenaPythonSQL
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Azure Data Engineering Pipeline

Built a cloud-native ETL solution using Microsoft Azure using Azure Data Factory, Azure Blob Storage, Azure Databricks, PySpark, SQL Developed Azure Data Factory pipelines. Integrated Databricks for distributed transformations. Prepared analytics-ready datasets. Automated cloud ingestion, and Improved scalability and reduced manual processing.

Azure Data FactoryAzure Blob StorageAzure DatabricksPySparkSQL
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Spark Big Data Processing

Developed distributed data processing workflows for large datasets using Apache Spark, PySpark, Spark SQL, Python. Built Spark ETL pipelines, Performed joins, aggregations, and transformations, and Benchmarked Spark against Pandas. Processed 100K+ records, and Improved scalability and execution efficiency.

Apache SparkPySparkSpark SQLPython
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Apache Cassandra Data Modeling & ETL Pipeline

Built an ETL and NoSQL solution using Apache Cassandra, Python, Pandas, and CSV. Developed a Python ETL pipeline to process and load music streaming data. Designed query-driven Cassandra tables using partition and clustering keys. Implemented efficient INSERT and SELECT queries without ALLOW FILTERING. Created analytics-ready data models for scalable song and user activity analysis.

Apache CassandraPythonPandasCSV
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