Data consultant with expertise in designing and implementing scalable data pipelines, cloud infrastructure, and analytics solutions. Experienced in building production-grade ETL/ELT systems using modern data stack technologies across AWS and GCP. Published researcher in quantitative finance with a strong foundation in mathematics, statistics, and computer science.

Current Focus: Building enterprise data platforms, real-time streaming architectures, and ML-driven analytics solutions for cross-functional business teams.

Data Engineering          Cloud Architecture        Data Science & ML
├─ ETL/ELT Pipelines     ├─ AWS Services          ├─ Statistical Modeling
├─ Data Modeling         ├─ Google Cloud Platform  ├─ Predictive Analytics
├─ Stream Processing     ├─ Infrastructure as Code ├─ Feature Engineering
├─ Data Quality          ├─ CI/CD Pipelines       └─ Model Deployment
└─ Orchestration         └─ Containerization

Python

SQL

R

Java

JavaScript

C#

C++

Bash

MATLAB

AWS

GCP

Docker

Terraform

GitHub

Git

Linux

Kafka

Spark

Databricks

dbt

Airbyte

Airflow

Delta Lake

PostgreSQL

MySQL

MongoDB

BigQuery

Snowflake

SQLAlchemy

Pandas

NumPy

Scikit-learn

TensorFlow

Matplotlib

Seaborn

Tableau

Power BI

HTML5

CSS3

React

Angular

.NET Core

FastAPI

Modern Data Stack Implementation | GCP + Airbyte + BigQuery + dbt + Airflow

Built an end-to-end analytics pipeline on Google Cloud Platform for digital media analytics, implementing a complete modern data stack architecture.

Architecture Highlights:

• Designed multi-source data ingestion using Airbyte with automated schema validation
• Developed layered data warehouse in BigQuery following medallion architecture
• Implemented SQL transformations using dbt Core with comprehensive testing and lineage tracking
• Orchestrated daily workflows with Apache Airflow for reliable, scheduled execution
• Achieved 99.9% pipeline reliability with automated monitoring and alerting

Tech Stack: GCP Airbyte BigQuery dbt Core Apache Airflow SQL Python

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Batch & Streaming Architecture | AWS + Kafka + Databricks + PySpark

Designed and implemented dual-mode data pipeline supporting both batch and real-time streaming for Pinterest-style data platform.

Technical Implementation:

• Built Apache Kafka producers for real-time API data ingestion
• Configured AWS Kinesis for stream processing with sub-second latency
• Developed PySpark transformations in Databricks for large-scale data processing
• Implemented Delta Lake tables with ACID transactions and schema enforcement
• Orchestrated complex workflows using Airflow on AWS MWAA
• Designed star schema data models optimized for analytical queries

Tech Stack: AWS (S3, RDS, Kinesis) Apache Kafka Databricks PySpark Delta Lake Airflow Python

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Cloud-Native Data Integration | Python + AWS RDS + PostgreSQL

Engineered production-ready ETL pipeline for extracting, transforming, and loading sales data from heterogeneous sources into cloud data warehouse.

Key Features:

• Developed Python ETL framework handling APIs, PDFs, JSON, and S3 sources
• Implemented robust error handling and data validation mechanisms
• Designed star schema in PostgreSQL on AWS RDS optimized for OLAP workloads
• Utilized SQLAlchemy ORM for database abstraction and connection pooling
• Automated data quality checks and anomaly detection

Tech Stack: Python AWS RDS PostgreSQL SQLAlchemy boto3 pandas tabula-py

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Quantitative Finance Research | Published in Peer-Reviewed Journal

Conducted comprehensive comparative analysis of stochastic models for stock price prediction, investigating the impact of historical data duration and volatility regimes on forecasting accuracy.

Research Contributions:

• Implemented and compared three stochastic models: Geometric Brownian Motion, Heston, and Merton Jump Diffusion
• Analyzed model performance across different market volatility conditions
• Examined optimal historical data windows for accurate predictions
• Published findings in Quantitative Finance and Economics, Volume 9, Issue 3, 2025

Tech Stack: Python NumPy SciPy Matplotlib Quantitative Finance

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Healthcare Analytics & Machine Learning | Python + ML

Developed machine learning models to predict ICU admission during COVID-19 pandemic, enabling proactive resource allocation and capacity planning.

Analytics Approach:

• Conducted exploratory data analysis on large-scale ICU admission records
• Engineered clinical and demographic features for predictive modeling
• Implemented ensemble methods using XGBoost and TensorFlow
• Achieved high prediction accuracy for resource demand forecasting
• Validated models using cross-validation and holdout testing

Tech Stack: Python pandas scikit-learn XGBoost TensorFlow matplotlib seaborn

Data Consultant | AiCore | Jun 2025 – Present

• Architect and implement production data pipelines using Python, SQL, Spark, and cloud platforms (AWS/Azure)
• Design and deploy data lakes and warehouses with CI/CD automation
• Build analytics dashboards and reports using Power BI and Tableau for business intelligence
• Apply DevSecOps, MLOps, and data governance best practices across enterprise projects

Mathematics & Computer Science Tutor | Oxford International Education Group | Feb 2025 – Present

• Deliver lectures and tutorials across six foundation-level modules for international students
• Design inclusive curriculum and digital teaching materials for diverse learning backgrounds
• Assess student performance through coursework, coding assignments, and technical presentations

Data Science Placement | NHS England (NHSE) | Jun 2025

• Completed a cancer-focused data science project as part of work experience placement at NHSE, using synthetic NHS Simulacrum data.
• Applied core healthcare data science methods including exploratory data analysis, feature engineering, and machine learning model development.

Trainee Software, Cloud & Data Engineer | AiCore | Dec 2024 – May 2025

• Completed intensive programme in software engineering, data engineering, and cloud architecture
• Built production-scale batch and streaming pipelines using Kafka, Databricks, Airflow, and AWS
• Delivered capstone projects demonstrating full-stack data pipeline design and deployment

Mulualem Kahssay & Shihan Miah (2025)
A Comparative Analysis of Stochastic Models for Stock Price Forecasting: The Influence of Historical Data Duration and Volatility Regimes
Quantitative Finance and Economics, 9(3), 602–630
DOI: 10.3934/QFE.2025021

• AiCore Certificate in Cloud & Data Engineering
• AiCore Certificate in Software Engineering
• BTEC Certificate in Work Skills
• CITB Certificate in Health and Safety
• BCS Certificate in Digital Skills