KD Pharma is looking to modernize its current data and reporting environment and establish a scalable, maintainable Microsoft-based data platform supporting multiple business systems and reporting needs.
The current landscape spans multiple source systems across Dynamics NAV, Business Central SaaS, QuickBooks, Azure SQL, Excel/SharePoint and other integrations. Group-wide reporting currently relies heavily on Power BI, with significant transformation and business logic implemented directly within Power BI models and dataflows.
The engagement will initially start with a 6-week Discovery Phase, focused on understanding the existing data estate and defining the target architecture, platform approach and implementation roadmap.
During Discovery, the team will:
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Assess the existing data landscape, integrations, data flows and Power BI dependencies
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Reverse-engineer and document critical parts of the existing reporting and data environment
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Identify key architectural, data-quality and integration gaps
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Design the target bronze / silver / gold architecture and dimensional data models
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Evaluate Microsoft Fabric, Azure Data Factory and Databricks and recommend the most suitable approach
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Define how transformation logic currently embedded in Power BI can be progressively moved into a proper upstream data-processing layer
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Stabilize critical parts of the existing reporting environment where required during the transition
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Define the first implementation / PoC scope, with the group sales reporting flow expected to be one of the key end-to-end use cases
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Define the roadmap for the subsequent build phase
If Discovery is successful and the client approves the implementation, the project is expected to continue into a longer-term build phase, starting with the agreed PoC and expanding into implementation of the wider data platform.
This is a highly hands-on Senior Data / Power BI Engineer role combining data engineering, Power BI platform expertise and technical discovery.
You will work closely with the Solution Architect, Delivery Manager, Azure DevOps Engineer and client stakeholders to understand the current environment, challenge existing patterns and help define a practical target architecture.
A key objective is to move transformation and data-preparation logic out of complex Power BI models and into scalable upstream pipelines and properly modelled fact/dimension tables, while ensuring that existing business-critical reporting continues to operate during the transition.