The successful candidate will be responsible for designing, building, and maintaining the organisation's data infrastructure to support high-quality Business Intelligence (BI) and analytics across all departments. He/She will ensure that data from operational and financial systems is reliably captured, cleaned, structured, and transformed into intuitive dashboards and analytical tools that empower data-driven decision-making across the group's properties and leadership teams.
Key Responsibilities.
Design, build, and maintain ETL/ELT data pipelines across multiple systems (PMS, POS, CRM, procurement, HR, financial systems).
Develop and optimise data models (star/snowflake schemas) and maintain the data warehouse/data lake as the organisation's single source of truth.
Ingest, clean, validate, and monitor data quality, ensuring consistency, completeness, and reliability across all datasets.
Build and enhance BI dashboards and reports (Power BI, Tableau, Looker, Qlik) that are user-friendly, commercially relevant, and visually intuitive.
Translate business questions into BI requirements, ensuring accurate SQL/DAX logic and consistently applied data definitions.
Collaborate with Finance, Operations, Commercial, HR, and Technology teams to understand data needs and deliver actionable technical solutions.
Create reusable data assets such as semantic layers, data marts, feature stores, and validated master datasets.
Document data flows, transformation logic, system architecture, and maintain an updated data catalogue.
Ensure data governance, security compliance, and implement best practices for metadata management.
Automate repetitive data tasks, identify improvement opportunities, and drive continuous enhancement of BI and analytics systems.
Key Qualifications.
Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related field.
Minimum of 5 years' experience in data engineering, BI development, or backend data architecture roles.
Strong experience designing ETL/ELT pipelines and working with modern data warehousing technologies.
Proficiency in SQL, Python, and data modelling concepts (dimensional modelling, semantic layers).
Hands-on experience with BI tools such as Power BI, Tableau, Looker, or Qlik.
Strong understanding of database systems (SQL & NoSQL) and cloud data environments.
Experience managing master datasets and integrating data across multiple operational systems.
Excellent analytical skills with the ability to translate business needs into technical solutions.
Strong documentation skills and familiarity with data catalogues, data lineage, and governance frameworks.
Experience working with automation, process optimisation, and workflow efficiencies