Departments / Data Platform & Governance
Data governance, quality, master-data management, and the platform infrastructure every analytics and AI effort depends on.
Applying our data-analytics toolkit to public blockchain data — on-chain analysis, transaction patterns — as a genuinely distinct analytics domain.
Python/SQL · On-chain data analysis · Data visualisation · Blockchain fundamentals + 1 more
Beyond dashboards — decision intelligence combines data, models, and business context to actually recommend a decision, not just report a number.
Python/R · Statistics · Decision-modelling frameworks · Business-problem framing + 1 more
Turning raw platform data — applications, learning progress, HR metrics — into dashboards that actually help people make decisions, not just look impressive.
Data visualisation tools (Power BI/Tableau/Looker or equivalent) · SQL · Visual design principles · Stakeholder communication + 1 more
Supporting the shared infrastructure — warehouses, access layers, tooling — that every team's analytics and AI work depends on.
SQL · Data warehousing concepts · Cloud data platforms basics · Documentation + 1 more
As our platform data grows (applications, learning events, assessments), building pipelines that scale becomes a real engineering problem, not a theoretical one.
Python/SQL · Data pipeline tools (Airflow or equivalent) · Distributed data-processing basics · Performance optimisation + 1 more
Building automated checks that catch bad data before it reaches a dashboard or a decision — a foundational discipline for every downstream analytics and AI effort.
SQL · Python · Data-quality frameworks (Great Expectations or equivalent) · Root-cause analysis + 1 more
Master data management — keeping one trustworthy version of core entities (users, roles, institutions) instead of conflicting copies scattered across systems.
MDM fundamentals · Data matching/de-duplication · SQL · Data modelling basics + 1 more
Hands-on data stewardship — the day-to-day work of keeping datasets accurate, documented, and trustworthy for the teams that rely on them.
Data stewardship practices · Metadata documentation · SQL basics · Data-quality checks + 1 more
As our data footprint grows across applications, HR, and research, data governance — ownership, access policy, retention — becomes a genuine operational need.
Data governance fundamentals · Data mapping · Policy documentation · Stakeholder coordination + 1 more