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Wesley Hucker | Data Analytics & AI Specialist
Blog

Hi, I'm Wes!

Marketing Data Scientist & Analytics Engineer — building scalable data systems, attribution models & ML for growth.
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Work History

  1. Senior Marketing Data Scientist — Innocean (Current)

  2. Digital Data Specialist — Monks (Jan 2023 – Jan 2025)

  3. Data & Media Specialist — Woolworths Cartology (Jul 2022 – Dec 2022)

  4. Data & Media Trader — Woolworths Cartology (Jul 2020 – Jun 2022)

  5. Media Trader & Data Analytics — Woolworths BWS (Jul 2019 – Jun 2020)

  6. Data Analyst — Colombus Agency (Jul 2017 – Nov 2018)

Technical Expertise

Data & Analytics Engineering

Advanced SQL data modeling and transformations, Python automation, ETL/ELT pipeline development, and analytical data modeling.

Cloud Data Platforms

Google Cloud Platform (BigQuery) for cloud data warehousing and large-scale dataset processing.

AI & LLMs

Building with LLM APIs, local models via Ollama, RAG pipelines, and MCP servers.

Business Intelligence

Looker Studio semantic modeling, dashboard architecture, and BI layers for self-service analytics.

Core Achievements

  • Designed and implemented end-to-end analytics data pipelines integrating GA4, advertising platforms, CRM systems, and offline conversion data into BigQuery, enabling unified cross-channel performance analysis.
  • Developed and optimised SQL-based data models and transformations in BigQuery to create analytics-ready datasets supporting reporting, attribution modelling, and performance optimisation.
  • Orchestrated and led the creation of a full-funnel business intelligence reporting framework connecting online and offline media spend, GA4 user journeys, CRM lead data, and actualised sales outcomes into a single analytical view.
  • Productionised machine learning models (clustering and predictive modelling) to optimise audience targeting strategies and reduce customer acquisition costs across large-scale marketing campaigns.
  • Engineered server-side tracking and GA4 Measurement Protocol integrations to improve event accuracy, cross-platform attribution, and reliability of downstream analytics datasets.
  • Built scalable marketing data architectures and analytical models used to inform multi-channel budget allocation, campaign optimisation, and performance forecasting.
  • Automated campaign data ingestion, transformation, and reporting workflows using Python and cloud-based data processing, reducing manual reporting effort by 40%+.