Comprehensive Data Engineering for EV Population in SQL

Comprehensive Data Engineering for EV Population in SQLComprehensive Data Engineering for EV Population in SQL

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Samuel Maina

• 18h

SQL Data Engineering: Cleaning & Structuring the EV Dataset

Before building the frontend visualizations, I handled the end-to-end data engineering and cleaning for the Electric Vehicle population data using PostgreSQL/SQL syntax within DBeaver.

Technical Database Operations Performed:

Data Auditing & Profiling: Utilized SELECT DISTINCT queries across core categorical fields (County, City, State) to identify structural inconsistencies, data entry anomalies, and trailing whitespaces.

Schema Alteration: Executed ALTER TABLE commands to structurally expand the staging database layout, introducing new calculated columns like State_Name to handle descriptive data mapping.

Conditional Data Standardization: Wrote robust, multi-conditional UPDATE scripts paired with extensive CASE WHEN logic to map and standardize short state abbreviations into full, clean geographical records (e.g., transforming 'ak' to 'Alaska', 'ca' to 'California').

Optimization for BI: Structured the final staging views to minimize overhead, ensuring seamless, high-performance importing and star-schema modeling once connected to Power BI.

Data Analysis Database Development MySQL

Ishita Guha Roy

• Jun 25

Data Cleaning & Feature Engineering Using Python

Python Data Analysis Data Engineering Web Scraping

Liudmila Davydiuk

• Jun 24

Designed and built a full data pipeline — pulling from multiple sources, resolving inconsistencies, enriching with external variables, and delivering clean outputs through interactive Power BI dashboards stakeholders can actually act on. Designed and built a full data pipeline — pulling from multiple sources, resolving inconsistencies, enriching with external variables, and delivering clean outputs through interactive Power BI dashboards stakeholders can actually act on.

The starting point was a large, structurally complex dataset with inconsistent formats, missing values, and misaligned fields. The goal was a repeatable pipeline that transformed it into something reliable enough to drive real decisions.

The enrichment layer was the differentiator: layering in demographic and contextual variables from external sources to add depth that the raw data alone couldn't provide. The final output was a Power BI dashboard built for non-technical audiences — clear enough to act on, rigorous enough to hold up to scrutiny.

AI-assisted validation (Claude Enterprise) was used throughout to cross-check outputs and flag inconsistencies before delivery

Data Analysis Data Visualization Research JSON PowerDB Python SQL

Brian Waizer

• Jun 24

Developed an automated data analysis solution to process complex logistics and supply chain datasets to identify operational inefficiencies. By writing custom Python scripts to parse, clean, and analyze historical transfer data, I built an automated reporting workflow that surfaces key optimization opportunities for stakeholders. The final output provides clear, actionable insights into asset redistribution. Engineered robust, automated ETL pipelines and custom tracking workflows to manage and optimize decentralized assets across a large network of facilities. This project involved consolidating diverse, messy data streams from multiple warehouse locations into a single, cohesive source of truth. Built dynamic Power BI dashboards on top of this clean data to give executive leadership complete visibility into inventory health and demand forecasting. Architected and deployed a highly scalable backend solution designed to power internal business utilities and automation tools. This project utilized a modern, robust 6-microservice architecture to ensure high availability, efficient data processing, and seamless tool integration. Managed the entire deployment process across a hybrid cloud infrastructure to ensure stable performance and security.