Data Engineering · 2023
Data Engineering & Visualization Pipelines
Data Glacier · London
01
Problem
Raw datasets needed reliable transformation and clear visualization to support downstream analysis and stakeholder presentation.
02
Challenge
Build data-pipeline and engineering fluency—Git workflows, exploratory data analysis, and multi-tool visualization—while contributing to live project work under deadline.
03
Solution
Applied data-pipeline and engineering principles to transform and manipulate datasets for analysis, using Git for version control and Power BI, Tableau, Matplotlib, and Plotly for visualization and presentation.
04
Impact
Strengthened the data-engineering and visualization foundation later applied directly to Orcawise's ETL pipeline work and to consulting engagements' analytics deliverables.
Approach
- 01
Set up Git-based version control for reproducible dataset transformations.
- 02
Ran exploratory data analysis to surface data-quality issues before building downstream pipelines.
- 03
Built transformation scripts to reshape datasets into analysis-ready structures.
- 04
Produced visualizations in Power BI, Tableau, Matplotlib, and Plotly matched to each stakeholder's preferred tool.
Outcomes
- Reproducible, Git-versioned data transformation workflows
- Multi-tool visualization fluency (Power BI, Tableau, Matplotlib, Plotly)
- Contributions to ongoing project work delivered on deadline
- Data-engineering foundation reused across later ETL and consulting analytics work