This is a transformation role. The successful candidate will:
●​Re-architect and stabilise ingestion data and shared application infrastructure
●​Improve proprietary valuation and comparable models
●​Improving integration with product development
●​Embed governance and quality frameworks
●​Define a forward-looking data and AI roadmap
The role requires a rare combination of:
●​Strong Data Engineering and Architecture experience
●​Strong Data Science expertise (including generative and model deployment in
production environments)
●​Experience leading leading, upskilling and growing data teams
●​Commercial instinct - turning data into product differentiation
This is not a maintenance role. It is foundational to our long-term competitive
moat.
 
 
The right candidate will be capable of operating at a strategic level while remaining
hands-on, and has the potential to grow into broader data leadership as the business scales.
Technical platform notes
-​Platform - > AWS / On-Prem Hybrid
-​Configuration -> IaaC (Terrafrom ) / Jenkins
-​Databases - > MySQL / Elastic (Opensearch) / Redis
-​Source Control -> Git (Github / legacy Bitbucket)
-​Infrastructure -> Airflow / DBT / MLFlow
-​Languages -> Php / Python / Javascript
Job Spec & Requirements
Head of Data

The Opportunity

Data is our core strategic asset. We ingest, structure and model large volumes of property
data to power proprietary insights, valuation models and enterprise-grade products.
We are now seeking a hands-on, high-calibre Head of Data to modernise and scale our data
function - transforming a complex, legacy-influenced estate into a resilient, AI-ready platform
that underpins our next phase of growth.
This is a build-and-elevate role. The successful candidate will stabilise and modernise our
architecture, raise technical standards across data engineering and data science, and lay
the foundation for a long-term, defensible data advantage.
 

The Role
1. Data Strategy & Architecture
●​Define and execute a modern data architecture strategy
●​Transition from disparate systems to a connected, scalable and pipelineable data
platform
●​Improve reliability, observability and reproducibility across ingestion and
transformation
●​Establish clear standards for data modelling, version control and deployment
You will create the foundation for a robust, AI-ready data platform capable of supporting
large-scale modelling and enterprise-grade products.
2. Ingestion & Data Engineering
●​Strengthen and future-proof large-scale crawling and scraping infrastructure
●​Improve ingestion reliability, latency and data completeness
●​Implement scalable pipelines and monitoring
●​Reduce manual intervention and fragile processes
Given the scale and complexity of our external data sources, ingestion robustness is critical
to competitive advantage.
3. Data Science & AI Capability
●​Lead and develop our data science and modelling function
●​Improve proprietary models including valuation engines, comparable analysis and
enrichment systems
●​Introduce modern ML practices, MLOps standards and model performance
monitoring
●​Identify practical, commercially valuable applications of AI across product and
operations
You will move the organisation from reporting-led data use to predictive, inference-driven
intelligence.
4. Data Quality, Governance & Trust
●​Establish clear data quality frameworks and metrics
●​Improve completeness and accuracy of critical property attributes
●​Implement governance, lineage and auditability standards
●​Ensure compliance with relevant data regulations and licensing requirements
Trust and defensibility of our data are central to enterprise credibility.
5. Team Leadership & Capability Building
●​Lead and develop a team of data scientists and data engineers
●​Raise hiring standards and define a modern data talent profile
●​Upskill existing team members in current best practices
●​Create a high-performance, outcome-driven data culture
You will balance technical depth with pragmatic delivery.
6. Commercial & Cross-Functional Partnership
●​Partner closely with Product, Engineering and Commercial teams
●​Translate data capability into product differentiation
●​Support enterprise conversations where data depth and credibility are critical​
●​Define a three-year data and AI roadmap aligned to company strategy
This role is not purely technical - it requires strong commercial instinct and strategic thinking.
 
What We're Looking For
Experience
●​Proven experience modernising complex data estates
●​Strong background in data engineering, warehousing and pipeline architecture
●​Experience leading and developing data science and engineering teams
●​Track record of deploying machine learning models into production
●​Experience improving data quality across large, messy external datasets
●​Exposure to enterprise environments where data credibility matters​
Desirable:
●​Experience in property, geospatial, marketplace or data-aggregation businesses
●​Experience working with large-scale web ingestion systems
●​Experience building AI-enabled products in production environments​
Leadership Profile
●​Comfortable operating hands-on while setting strategic direction
●​Pragmatic, outcome-focused and resilient in legacy environments
●​Strong communicator able to translate technical complexity into commercial impact
●​Ambitious - motivated to build a long-term data advantage, not just maintain systems
Success in the First 12-18 Months
Success will include:
●​Delivery of a stabilised, scalable and well-documented data architecture
●​Measurable improvements in data quality across core property attributes
●​Deployment of improved ML models with demonstrable uplift in accuracy or business
impact
●​Clear governance framework and quality metrics in place
●​Stronger technical standards and capability within the data team
●​A defined 3-year data and AI roadmap
This role has the potential to evolve into a broader strategic data leadership position as the
company scales.