Engagement Overview
Led the delivery of Eseye’s market-leading IoT connectivity platform, overseeing a complex migration from on-premise infrastructure to a cloud-native architecture.
Platform Development
Problem
The organisation was transitioning from an on-premises environment to a AWS cloud hosted platform; however, the organisation lacked a clear product strategy and well-defined requirements. Delivery priorities were unclear, requirements entered without a structured intake or validation process, and development capacity was primarily offshore, resulting in misunderstood requirements and late-stage delivery gaps.
As a result, the platform was being delivered largely on a like-for-like basis with the legacy solution, rather than being refined to meet user needs or align with modern platform best practices, projects were delivered but were not meeting expectation. A disconnect between product and engineering further limited alignment, leading to inconsistent expectations, slow decision-making, and a lack of shared strategic direction.
Outcome
Delivered critical platform capabilities aligned to user and operational needs, including dashboards, alerting, and notification management, while redefining existing capabilities to improve usability, performance, and non-functional characteristics. This allowed the organisation to secure additional clients and deliver against the agreed statements of work.
Re-defined the platform roadmap, creating a realistic two-year delivery plan that balanced new capability development, underlying platform development (APIs, ETLS, SOLR, Data) with the reduction of accumulated technical debt, enabling predictable, iterative and consistent delivery.
Established end-to-end product delivery ownership for Product Managers, including setting clear responsibilities, such as drafting statements of work, providing detailed product requirements, and owning the collaboration between internal stakeholders and teams, as well as clients. This enabled multiple delivery teams to deliver solutions that met expectations the first time.
Introduced a product-centric delivery model, clarifying ownership across product areas and embedding structured requirement definition through wireframes, acceptance criteria, and non-functional requirements. This enabled earlier engineering engagement, reduced late-stage rework, and materially limited scope creep.
Implemented a unified requirements intake and governance process, establishing a single mechanism for capturing, triaging, and prioritising feature requests and defects. This enabled full traceability from inception through delivery, significantly reducing missed requirements and late discovery during development.
Reporting, Analytics & AI
Problem
The existing on-premises reporting solution did not scale as client adoption increased. Reporting was constrained to fixed schedules, struggled with large data volumes, and produced static, PDF-style outputs that failed to meet user needs or deliver meaningful insight. Previous attempts to improve the solution lacked clear product ownership and coordination, resulting in incremental changes that did not address the underlying scalability and usability issues.
Outcome
Designed and delivered a scalable reporting architecture to replace the legacy on-premises solution, removing existing performance bottlenecks and enabling the platform to scale with client growth. The reporting estate was rationalised into a clear MVP, with redundant reports removed and cloud-based data pipelines established to support performant, query-driven reporting.
Introduced product-led ownership and strategic direction to the reporting capability, defining a two-year roadmap aligned to user needs and commercial priorities. This shifted reporting from static, scheduled outputs to an on-demand model capable of handling large report requests and evolving requirements.
Enabled flexible, on-demand reporting at scale, introducing structured data aggregation and search-based retrieval (SOLR) to support large and complex report requests. This removed dependency on fixed schedules and significantly improved the usability and responsiveness of the reporting platform.
Defined the end-to-end reporting experience, bridging backend capability with user interaction through clear UI interaction models. This enabled users to access, download, schedule, and request reports on demand, delivering a fully integrated reporting solution rather than a standalone data engine.
Delivered a real-time customer dashboard capability, providing an immediate view of each customer’s estate. The dashboard was iteratively evolved to support configurable widgets, comparative analysis, and forecasting, establishing the foundation for a modular, marketplace-style analytics model.