Source description
About the role
- Data Governance & Democratization
Single Source of Truth (SSoT): Establish a Ubiquitous Language across the organisation to ensure metrics like are defined and interpreted identically across all departments.
Standardisation Framework: Implement and oversee a strong Data Architecture within our DataWarehouse to ensure data quality and lineage.
Data Cataloging: Deploy and maintain a centralised Data Catalog that treats data as a governed product, ensuring every dataset has an owner, a definition, and a clear business purpose.
- Data "Self-Serve" & Data as a Product
Frictionless Access: Shift the organisation from "Access to Tables" to "Access to Information" by building high-context data models that reflect business logic rather than raw database schemas.
Product Thinking: Apply product management principles to data assets, gathering requirements, iterating on user feedback, and measuring the "internal NPS" of our data tools.
- Organisational Design & Strategy
Operating Model: Design and evolve the data team structure (Centralised vs. Embedded) to ensure analysts and scientist are close to the business problems they solve while maintaining central technical standards.
Strategic Bandwidth: Structurally protect the team's time, ensuring a healthy split between high-impact Strategic Initiatives and essential operational support or maintenance.
Talent Development: Define roles and career paths for a modern data stack, focusing on the rise of Analytics Engineers and Data Strategists .
- Data Up-skilling & Culture
Literacy Programs: Lead the internal "Data Academy" to up-skill non-technical stakeholders, moving the company from a "Launch and See" culture to a Hypothesis-Driven model.
Experimentation Framework: Evangelise a tiered testing strategy (Fake Door, MVEs, and Full A/B testing) to ensure resources are allocated based on the maturity of the business assumption.
- Platform Engineering & AI Tranformation
Engineering Collaboration: Partner with the Platform Engineering team to design a scalable data platform, ensuring seamless data ingestion and orchestration.
AI Transformation: Lead the transition into the AI Analytics Era by integrating modern AI based tooling into the data workflow, automating routine insights, anomaly detection, and natural language querying for stakeholders.
Tooling Optimisation: Evaluate and deploy AI-assisted engineering tools to maximise the team's "time-to-insight" and minimise manual ETL overhead.
- Transition to Business Partnership
Model Evolution: Lead the structural shift from a reactive "service" model to a proactive "business-partnering" framework. Move the team beyond fulfilling ad-hoc reporting requests toward embedded strategic collaboration.
Predictive Integration: Champion the use of predictive analytics and machine learning to drive business outcomes. Instead of just measuring past performance, empower departments (Sales, Marketing, Ops) with predictions, forecasting to guide their daily decision-making.
Value Attribution: Partner with functional leads to define KPIs that measure the impact of data insights on the P&L, ensuring the data team is viewed as a value-generator rather than a cost center.
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