I’m passionate about building reliable data systems that turn raw, scattered data into clean, trusted, and analytics-ready datasets. My main focus is Data Engineering and Analytics Engineering, with a strong interest in data quality, pipeline reliability, scalable data models, and KPI-ready reporting layers.
I also have knowledge of Data Analytics, Data Science, and Machine Learning, including exploratory analysis, feature preparation, classification, regression, clustering, and preparing model-ready datasets.
- Build end-to-end ETL/ELT pipelines
- Design clean and reliable data models
- Create staging, intermediate, canonical, and mart layers
- Develop KPI-ready tables for dashboards and reporting
- Perform data quality checks, validation, deduplication, and reconciliation
- Work with structured, semi-structured, and API-based data
- Transform raw data into trusted datasets for analytics and decision-making
- Prepare clean datasets for analytics, reporting, and machine learning workflows.
- Programming: Python, SQL
- Databases & Warehouses: PostgreSQL, BigQuery, MySQL, MongoDB
- Data Engineering: ETL/ELT, REST APIs, incremental loads, data validation, data modeling
- Orchestration & Cloud: Apache Airflow, AWS Lambda, EventBridge, CloudWatch, S3
- Analytics & BI: Power BI, Domo, KPI reporting, dashboard-ready marts
- Data Science & ML: pandas, exploratory data analysis, feature preparation, classification, regression, clustering
⚡ "Turning raw data into robust systems and actionable insights is what excites me the most!"