Behavioural Data Analyst at GPost
ABOUT THE OPPORTUNITY
We are a purpose-led startup building AI-powered platforms that serve People, Communities, and Businesses. Our commercial model centres on delivering rich, community-level consumer behaviour intelligence to large retail partners — intelligence that is ethically sourced, privacy-respecting, and genuinely actionable.
As our founding Behavioural Data Analyst, you will architect the data layer that powers everything. You will build and maintain P2-classified (non-sensitive) data tables, source publicly and legally available datasets from the web, match and enrich records against our DARS (Decentralised Authentication Registry Service) registered sources, and develop the algorithms that transform messy, unstructured data into clean, structured consumer behaviour intelligence — ready for B2B on-selling to major retailers.
This is a ground-floor role. You will define standards, pipelines, and processes from scratch — working together with our AI Researcher, broader team, Partners and CEO in a close, high-trust team.
Data Governance Context
DARS (Decentralised Authentication Registry Service): Our registered data governance framework governing what data we hold, how it is sourced, and who may access it. All data pipelines you build must align to DARS-registered sources and approved use cases.
P2 Classification: Non-sensitive, aggregated or de-identified data. No personally identifiable information (PII). Includes publicly available datasets, aggregate community statistics, behavioural trend data, and open government or commercial datasets sourced with appropriate rights.
WHAT YOU WILL DO
1 · Data Architecture & Table Design
▸ Design and maintain structured P2-classified data tables that serve as the single source of truth for consumer behaviour intelligence.
▸ Define data schemas, naming conventions, versioning standards, and documentation practices from day one.
▸ Build scalable pipelines to ingest, validate, and refresh data across multiple sources.
2 · Open Data Sourcing & DARS Matching
▸ Identify, evaluate, and onboard freely and legally available datasets from the web — government open data portals, census repositories, public commercial datasets, and other DARS-approved external sources.
▸ Match and enrich externally sourced records against our DARS-registered data, ensuring lineage, compliance, and full auditability.
▸ Maintain a living register of approved external data sources, licensing terms, refresh cadences, and quality scores.
▸ Work closely with legal and compliance advisors to ensure every data source meets our ethical and regulatory standards.
3 · Unstructured-to-Structured Algorithms
▸ Build and iterate on algorithms that extract signal from unstructured data (text, web content, social metadata, survey responses) and convert it into clean, structured behavioural attributes.
▸ Apply NLP, classification, clustering, and entity resolution techniques to produce community-level consumer profiles.
▸ Collaborate with our AI Researcher to integrate these algorithms into the broader AI and orchestration stack.
4 · Community-Level Consumer Intelligence
▸ Aggregate and model behavioural data at the community level (suburb, postcode, demographic cohort) — matched to an individual level, non-sensitive, to surface actionable retail insights.
▸ Develop segmentation frameworks that reveal how purchasing intent, lifestyle patterns, and category preferences vary across communities.
▸ Produce repeatable, auditable data products that can be packaged and delivered to large retail B2B partners.
5 · B2B Data Product Delivery
▸ Work with the CEO & team to understand retailer use cases, “virtual planogram decisions”, range localisation, promotional targeting, community forecasting — and shape the data product accordingly.
▸ Design output formats (APIs, data feeds, dashboards, reports) that are ready for integration into retailer systems.
▸ Support pre-sales and partner onboarding with data samples, methodology documentation, and technical briefings.
WHAT WE ARE LOOKING FOR
Core Technical Skills
▸ Min 3–6 years of experience in data analysis, behavioural analytics, or consumer intelligence roles.
▸ Proficiency in Python (pandas, NumPy, scikit-learn) and SQL for data wrangling, pipeline construction, and analysis.
▸ Demonstrated experience sourcing and integrating open/public datasets — government portals, ABS/Census equivalents, open commercial data — with proper licensing diligence.
▸ Experience building NLP or text-processing pipelines to convert unstructured content into structured attributes (entity extraction, classification, sentiment, topic modelling).
▸ Strong knowledge of data governance principles — data lineage, classification frameworks (e.g., P1/P2/P3), audit trails, and access controls.
▸ Familiarity with data matching, record linkage, and entity resolution techniques across heterogeneous datasets.
▸ Experience with community or geographic-level data aggregation — working with postcode, SA2/SA3, or equivalent spatial hierarchies.
Data & Analytics Tooling
▸ Cloud data platforms: AWS (S3, Glue, Athena), GCP (BigQuery), or Azure — at least one at production scale.
▸ Data pipeline / orchestration tools: Airflow, dbt, Prefect, or similar.
▸ Visualisation: experience producing clear, insight-led outputs in Tableau, Power BI, Looker, or Python-based tooling.
▸ Version control: Git-based workflows for code and, ideally, data versioning.
Mindset & Values
▸ A genuine commitment to ethical data practice, you treat data governance as a feature, not a constraint.
▸ Comfortable working in ambiguity; you design structure where none yet exists.
▸ A community-first perspective — you understand that behind every data point is a real neighbourhood, household, or person.
▸ Collaborative, low-ego, and willing to both teach and learn in a small team.
▸ Commercial awareness: you understand how data insight translates into retailer value and revenue.
NICE TO HAVE
▸ Experience with Decentralised Data Tables, ADHA, or equivalent national data authorisation/registration frameworks.
▸ Background in retail analytics, shopper behaviour, or category management data.
▸ Knowledge of open data landscapes, in US/Europe & other emerging countries.
▸ Familiarity with privacy-enhancing technologies (PETs): differential privacy, synthetic data generation, or k-anonymity.
▸ Prior startup experience or demonstrated ability to build data infrastructure from the ground up.
▸ Experience preparing data products for commercial licensing or B2B distribution.
OUR DATA PRINCIPLES
Privacy by Design
P2 classification means no PII — ever. Aggregated, de-identified, and community-level insight only. 📋 DARS Compliance
Every data source is registered, licensed, and auditable. Governance is not an afterthought — it is the foundation. 🌏 Community First
Insight is most valuable when it serves the community it describes — not just the retailer buying it.
WHY JOIN US
Build the Data Layer
You design the architecture. No inherited mess, no legacy tech debt — just a blank canvas and clear principles.
Real Commercial Impact
Your data products directly drive retailer decisions — range, pricing, promotions — at a community level that matters.
Direct CEO Collaboration
Work closely with our AI Researcher and CEO — your insight shapes product, not just a dashboard nobody reads.
Ethics + Commerce
Prove that responsible data practice and strong commercial outcomes are not trade-offs — they reinforce each other.
Our Values: Simplicity · Consistency · Passion
Data that does GOOD — and builds a real commercial business doing it.
HOW TO APPLY
We review every application personally. Please send the following to our hiring team:
▸ Your max 2-page CV or LinkedIn profile.
▸ A brief note (3–5 sentences) on your approach to ethical data sourcing and governance.
▸ Examples of data products, pipelines, or analytical frameworks you have built — links, case studies, or a short-written summary.
▸ Email: kevin.chetty@gpostcorp.com
We are an equal opportunity employer committed to building a team as diverse as the communities we serve.