• Discover, explore, and process data from various sources (relational databases, flat files such as CSV, YML, XLS), forming a deep understanding of content, limitations, and business relevance.
• Identify, investigate, and clearly articulate data quality, completeness, and consistency issues, including their downstream impact on analytics and AI use cases.
• Challenge data provenance and assumptions in legacy datasets, reframing against current needs
• Translate business questions and operational needs into meaningful KPIs, metrics, dashboards, and analytical narratives consumable by non-technical stakeholders.
• Create clear metadata and documentation that explains datasets, transformations, assumptions, and analytical outputs to support reuse and trust
• Partner closely with Data Scientists and Visualisation specialists to enable advanced analytics.
• Support the adoption of MRO AI Solutions within BA operational workflows by ensuring insights are actionable, timely, and well-embedded in decision-making processes.
• Design, build, and optimise data pipelines for ingestion, transformation, and storage.
• Ensure data quality, integrity, and security controls are applied across systems.
• Apply cloud and data‑engineering best practices pragmatically to ensure solutions scale where needed across OpCos, without over‑engineering
• Design data architectures and pipelines that support multi-OpCo deployment, ensuring modularity and interoperability.

• Strong experience in data analysis within a product or operational environment, with demonstrable impact on decision‑making
• Advanced hands-on experience with data processing and analysis tools (SQL, Python, Pandas, etc), with a bias towards exploration, insight and explanation
• Proven ability to understand legacy datasets/pipelines and to evaluate their fitness for new use cases
• Comfortable working independently and communicating with non-technical stakeholders

• Solid understanding of data modelling concepts and API-driven data integration to influence pipeline design
• Proven experience in developing, testing, and deploying data solutions into production environments, ensuring reliability, scalability, and maintainability beyond proof-of-concept or prototype stages.
• Practical expertise in Python, SQL, and modern ETL or orchestration frameworks.
• (Preferred) Skills in data visualisation (PowerBI, Tableau, and/or other dashboarding tools)
• (Preferred) Hands-on experience with cloud platforms, ideally AWS

• Significant experience in similar roles, with a proven ability to integrate quickly into new teams and deliver immediate value.
• Ability to design enterprise-grade data solutions under tight timelines.
• Strong stakeholder engagement and solution-oriented mindset.
• Track record of creating high-impact outcomes and driving stakeholder satisfaction from day one.
• Ability to implement standards and frameworks for scalable data solutions across multiple operating companies.
• Familiarity with airline or logistics data domains is a plus.

• Initial co-location with client teams in London is essential to ensure close collaboration.
• Candidates must also be prepared to occasionally travel internationally during later stages to facilitate group-wide deployment.

What you need to do now

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