I build scalable, compliant data platforms that unlock decision-ready insights.

Problem: Utility organisations manage sensitive customer and operational data, but often lack governance aligned with NDPA and audit expectations.

Approach: Developed a comprehensive framework covering data classification, RBAC, audit logging, compliance workflows, and risk management tailored to utility operations.

Technologies & Methods: Governance models, GDPR compliance mapping, access control design, audit and risk controls.

Outcome: Improved regulatory readiness, reduced data-handling risk, and established a scalable governance blueprint.

Problem: Large volumes of historical stock market data stored as CSV files on on-premises systems often require manual handling and lack automated, consistent processing workflows. This creates inefficiencies in ingestion, transformation, and storage, making it difficult to reliably prepare data for analysis in a scalable cloud environment.

Approach: An event-driven ETL pipeline was designed using AWS services and Apache Airflow to automate the ingestion, processing, and loading of stock CSV files into a cloud-hosted PostgreSQL database. File uploads to Amazon S3 trigger automated workflow execution, where data is consolidated, transformed using Python, and monitored through integrated alerting mechanisms.

Technologies: AWS EC2, S3, Amazon RDS, AWS Lambda, AWS SNS, Python(Pandas), Bash Scripting and S3FS-Fuse, Apache Airflow, Docker.

Outcome: Improved scalability and resilience, reduced infrastructure overhead, and enabled a cloud‑ready analytics foundation.

Problem: Inconsistent data quality created unreliable reporting and poor decision confidence.

Approach: Implemented profiling, quality checks, and automated validation rules embedded within processing workflows.

Technologies: Python, SQL, automated data testing concepts, quality metrics.

Outcome: Increased trust in datasets, reduced downstream failures, and standardised quality controls.

Problem: Manual collection and analysis slowed market intelligence and reduced insight timeliness.

Approach: Built an automated platform integrating scraping, validation, transformation, orchestration, API delivery, NLP insights, and dashboards.

Technologies: Python, Scrapy, PostgreSQL, Apache Airflow, NLP, Streamlit / BI tooling.

Outcome: End‑to‑end automation improved insight velocity, reliability, and scalability for analytics users.

Problem: Rural catchment areas often rely on periodic physical sampling to assess water quality, which is resource-intensive, slow, and can miss early signs of contamination. There is a need for a data-driven system that can use environmental, settlement, and chemical indicators to help identify potential water quality risks before field sampling takes place..

Approach: A data platform was developed to integrate geospatial, settlement, and physico-chemical water quality data into a structured ETL pipeline, with curated outputs visualised through an interactive Streamlit dashboard. The system enables exploration of historical trends, comparison of chemical levels, and basic predictive assessment of water suitability to support early decision-making for municipalities and environmental stakeholders.

Technologies: Python (Pandas, NumPy), Apache Airflow, AWS S3, Streamlit, AWS EC2, AWS RDS, Docker.

Outcome: Enabled timely analytics and operational responsiveness with scalable event-driven architecture.

Problem: Property records were siloed and hard to query consistently.

Approach: Built an ETL pipeline to standardise, transform, and load property datasets into PostgreSQL with analytics-friendly structure.

Technologies: Python, PostgreSQL, SQL optimisation.

Outcome: Streamlined access, faster reporting, and improved data usability for downstream applications.

I design and build scalable data platforms that make analytics and reporting reliable, fast, and maintainable — from ingestion to consumption layers.

I migrate data workloads from on-prem or legacy systems to cloud platforms using a phased approach that controls risk and protects data quality.

Tools

• AWS / Azure / GCP • Cloud storage & compute
• ETL/ELT tooling • Monitoring & logging

I engineer reliable pipelines that move and transform data for analytics and products — with strong validation, monitoring, and fault-tolerant patterns.

Tools

• Airflow • Kafka • Python • SQL

I implement governance and quality controls that make data compliant, auditable, and trusted across teams — aligned with NDPA/GDPR principles.

Best for

• Regulatory readiness
• Reducing data risk
• Improving trust in dashboards and analytics

I translate data into decision-ready insights through KPI design, dashboards, and stakeholder-friendly reporting layers.

Tools

• Power BI • SQL • Python • Streamlit

I build reusable data products and APIs that serve reliable datasets to teams, dashboards, and applications.

Tools

• Python • FastAPI • PostgreSQL