The stack behind the case studies.
Every tool listed here has been used on a real project. Below is the full breakdown of what each tool was used for, the case study where it was applied, and how the capability maps to the data analyst roles I'm targeting in Melbourne.
Databases & Querying
PostgreSQL and SQL Server. Joins, subqueries, CTEs, aggregations, window functions, and multi-table revenue and customer analysis.
Applied in Rockbuster SQL Customer & Revenue Analysis — 15-table relational database, customer value and geographic revenue queries.
Relational database design, schema analysis, data profiling, and query optimisation using pgAdmin.
Applied in Rockbuster — produced a formal data dictionary and documented SQL outputs for stakeholder presentation.
Used in production for six years to analyse paid-media campaign performance across Facebook, Google, TikTok, and Taboola.
Applied in Skynet World Trade (own eCommerce business) — campaign-level CPA, ROAS, and CTR analysis to scale winning ads and pause underperformers.
Microsoft SQL Server environment exposure through CareerFoundry coursework; relational analysis and querying.
Applied in CareerFoundry Data Analytics intensive — SQL querying and relational database modules.
Programming & Analysis
Data wrangling, cleaning, merging, EDA, descriptive statistics, hypothesis testing, and customer segmentation using Jupyter notebooks.
Applied in Retail Customer Segmentation — built a behavioural segmentation pipeline on 162K+ customers, producing 8 structured CSV outputs for downstream Power BI consumption.
DataFrame operations, transactional table merging, data type resolution, missing value handling, and feature engineering.
Applied in Retail Customer Segmentation, King County Real Estate, CitiBike NYC — all five Python case studies.
Numerical operations, array handling, and statistical computations supporting EDA workflows.
Applied in All Python-based case studies as part of the analytical pipeline.
Static visualisation for EDA: distribution plots, scatter plots, correlation heatmaps, and customer profile breakdowns.
Applied in King County Real Estate analysis, Retail Segmentation EDA phase, CDC Influenza analysis.
Reproducible analysis, narrative documentation alongside code, and stakeholder-shareable analytical workflows.
Applied in Every Python case study — used as the documentation and execution layer.
Visualisation & BI
Multi-page dashboard development, DAX measures, slicers, drill-down interactions, and stakeholder-ready visual storytelling.
Applied in Retail Customer Segmentation Dashboard — 11 strategic segments across 162K+ customers with regional and income-group filtering.
Dashboard design, geographic visualisations, time-series analysis, and presentation-ready outputs for non-technical stakeholders.
Applied in Rockbuster revenue dashboards, CDC Influenza Resource Planning, GameCo regional sales analysis.
Pivot tables, advanced formulas, data cleaning, descriptive statistics, and dashboard prototyping. Plus 18 years of operational cost and labour reporting.
Applied in GameCo Global Sales Analysis — multi-year sales dataset cleaned, validated, and analysed with executive-style recommendations.
Six years of self-directed use for web traffic analysis, conversion tracking, audience segmentation, and campaign attribution.
Applied in Skynet World Trade — three eCommerce stores measured end-to-end.
Marketing & Web Analytics
Tag deployment, event tracking, conversion setup, and tracking infrastructure across multi-platform eCommerce stores.
Applied in Three Shopify and WooCommerce stores — end-to-end tracking implementation.
Facebook Pixel, TikTok Pixel, and Taboola Pixel — full conversion measurement and audience-building infrastructure.
Applied in Skynet World Trade campaigns across four ad platforms.
CPA, ROAS, CTR, and ROI analysis across Meta, Google, TikTok, and Taboola. Decisions on scaling, pausing, and creative iteration.
Applied in Six years of paid-media management at approximately $1,000/month peak spend.
Data Processing & Techniques
Duplicate detection, missing value strategies, type coercion, structural integrity checks, and data quality assurance.
Applied in All five case studies — the prerequisite to every analysis.
Descriptive statistics, distribution analysis, correlation exploration, and pattern identification ahead of formal modelling.
Applied in King County Real Estate, Retail Segmentation, CDC Influenza — full EDA workflows.
Behavioural variable engineering, loyalty tiers, frequency bands, and segment classification for retention and reactivation strategies.
Applied in Retail Customer Segmentation — 11 strategic segments identifying a 45.2K-customer reactivation opportunity.
Statistical testing for differences across groups, confidence interval interpretation, and significance evaluation in analytical reports.
Applied in CareerFoundry coursework and CDC Influenza analysis.
How the stack maps to the jobs I'm applying for
Recruiters and hiring managers usually scan portfolios looking for a specific stack. Here's the direct map between role types and the capabilities each one draws from.
BI Reporting Analyst
Primary stack: Power BI, DAX, SQL, data modelling, dashboard delivery.
Demonstrated in the Retail Customer Segmentation Dashboard — multi-page Power BI with interactive slicers across 162K+ customer records.
Commercial Analyst
Primary stack: Excel, SQL, Power BI, KPI tracking, performance reporting, commercial reasoning.
Demonstrated in the Rockbuster revenue analysis and GameCo market analysis. Reinforced by 18 years of cost, labour, and margin management in commercial operations.
Customer & Performance Analyst
Primary stack: Python, SQL, Power BI, BigQuery, segmentation, campaign analytics.
Demonstrated in the Retail Customer Segmentation work and six years of hands-on eCommerce campaign analytics across Meta, Google, TikTok, and Taboola.
Data Quality & Operational Reporting
Primary stack: Python, SQL, Excel, validation pipelines, structured reporting.
Demonstrated across all five case studies — cleaning, structural integrity, and reproducible output generation are the prerequisite to every dashboard.
Foundational strengths from 18 years of commercial work
Tools are learnable in a year. Commercial judgement isn't. Here's what I bring beyond the technical stack — the things that turn analysis into decisions.
Stakeholder communication
Years of presenting numbers to operations managers, finance teams, and corporate clients who don't have time for jargon. Plain-language data storytelling.
Cost & margin literacy
Eighteen years of managing food cost, labour cost, and inventory in high-volume operations. I read P&L impact instinctively, not as an afterthought.
Operational pressure
Corporate Chef roles at JP Morgan Sydney and the Melbourne Convention & Exhibition Centre. Tight deadlines, high stakes, no excuses — the same posture I bring to analytics deliverables.
Commercial self-direction
Six years running my own eCommerce business while working full-time. Real money, real decisions, real consequences when the data was wrong.
Attention to detail
Kitchens punish sloppy work immediately. That discipline transfers directly to data validation, query accuracy, and report reliability.
Continuous learning
A full year of intensive CareerFoundry training, funded personally, completed alongside other commitments. Evidence of follow-through, not just enrolment.
See the skills in context
Every capability above maps to a specific case study with the business problem, the analytical approach, and the decision value documented in full.