Simon Ngugi

Data Engineer

Nairobi, Nairobi County, Kenya2 yrs 6 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Expert in cloud data solutions and ETL processes.
Proven track record in machine learning model optimization.
Strong collaboration skills with cross-functional teams.

Stackforce AI infers this person is a Data Engineer specializing in cloud-based data solutions and machine learning for SaaS applications.

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Skills

Core Skills

Data EngineeringCloud ComputingMachine Learning

Other Skills

AWSMicrosoft FabricAzure Data FactoryETLData GovernancePower BIData Qualityscikit-learnTensorFlowNLPData ArchitectsMicrosoft AzureData StreamingJavaData Models

About

(AI Data Engineer and AI Analytics Engineer) I'm a dynamic data nerd with proven ability to deliver short or long-term projects in data engineering, data warehousing, machine learning, and business intelligence realm. My passion is to deliver top-notch, scalable data solutions to provide immediate and lasting value. I specialize in the following data solutions: ✔️ Data strategy advisory & technology selection/recommendation ✔️ Building data warehouses using modern cloud platforms and technologies ✔️ Creating and automating data pipelines, real-time streaming & ETL processes ✔️ Building highly intuitive, interactive dashboards. ✔️ Data Cleaning, Processing, and Machine Learning models Some of the technologies I most frequently work with are: ☁️ Cloud: AWS, 👨‍💻 Databases: Redshift, Snowflake, RDS, PostgreSQL, MySQL, S3, MongoDB, Cloud Data Store, Redshift,cassandra ⚙️ Data Integration/ETL: AWS Glue & EMR,Apache Airflow, Airbyte, DBT, Apache kafka, Apache spark, Apache flink 📊 BI/Visualization: Tableau, PowerBI, Excel, AWS QuickSight 🤖 Machine learning - Natural Language Processing, Keras, Jupyter Notebook, Python, TensorFlow, Pandas, Numpy, Pytorch, scikit learn.

Experience

Save the children international

Data Engineer

Apr 2025 – Present · 11 mos

Enterprise Cloud Migration & Cost Optimization: collaborated on the end-to-end migration of enterprise data infrastructure from legacy SSIS/SSMS to Microsoft Fabric and Azure Data Factory (ADF). Successfully reduced total infrastructure costs while ensuring zero disruption to business operations and on time .
High-Volume ETL/ELT Pipeline Development: Collaborated on design and maintained secure, scalable data pipelines . Implemented a robust Medallion Architecture (Lakehouse) to streamline data ingestion, cleansing, and transformation for downstream analytics.
Financial & Operational Data Management: collaborated and worked on the modernization of three critical business systems by transitioning them to Azure Data Factory. These pipelines currently serve as the production backbone for global reporting across 50+ countries
Data Quality & Governance: Developed automated data quality checks and performance tuning scripts to ensure the accuracy and reliability of business-critical datasets, directly supporting the needs of the global finance team.
Analytics & Stakeholder Collaboration: Collaborated with cross-functional teams to build and refine semantic models and Power BI datasets. Translated complex stakeholder requirements into scalable data models that support operational and financial reporting.
Documentation & Knowledge Transfer: Established comprehensive process documentation and data lineage standards, streamlining the onboarding of new team members and ensuring compliance with internal data governance policies.

AWSMicrosoft FabricAzure Data FactoryETLData GovernancePower BI+2

Truss network

Machine Learning Engineer/DATA ENGINEER

Sep 2023 – Jun 2025 · 1 yr 9 mos · Nairobi County, Kenya

At Truss Network, I played a pivotal role in supporting data infrastructure and advancing machine learning initiatives.
My work focused on developing and optimizing data pipelines and building machine learning models that enhanced the company's AI-driven capabilities, aimed at merging data for co-founder
Model Development & Deployment: Designed and deployed machine learning models using scikit-learn and TensorFlow improving the startup's ability to predict key metrics and drive business decisions.
Natural Language Processing (NLP): Applied NLP techniques to analyze and extract insights from unstructured data sources, enriching data merging processes for co-founders.
Model Optimization: Fine-tuned models by implementing feature engineering, hyperparameter tuning, and cross-validation, increasing model accuracy by 8% over initial deployments.
Automation of Predictions: Automated prediction workflows for real-time data analysis, improving operational efficiency by reducing manual intervention by 9%
Key Achievements:
Increased model accuracy by 12% significantly enhancing the predictive capabilities of business strategies.
Reduced data processing time by 7% by streamlining data integration workflows.
Led the integration of AI-driven data merging tools, improving the startup's ability to merge and analyze founder data in real-time.