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Senior Data / Power BI Engineer | KD Pharma

GT
Формат:
повний remote
Рівень:
senior
Джерело:
jobs.dou.ua
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GT was founded in 2019 by a former Apple, Nest, and Google executive. GT’s mission is to connect the world’s best talent with product careers offered by high-growth companies in the UK, USA, Canada, Germany, and the Netherlands.

On behalf of KD Pharma, GT is looking for a Senior Data / Power BI Engineer with strong Azure data engineering experience, deep Power BI expertise, and the ability to independently reverse-engineer and modernize a complex, largely undocumented data and reporting environment.

**Expected Involvement: The engagement is expected to begin with a 6-week discovery phase of approximately 30 hours per week (180 hours total). Following successful completion of the discovery phase and client approval, there is potential to transition into a long-term, full-time implementation role.

About the Client

Founded in 1988, KD Pharma is a technology-driven CDMO (Contract Development & Manufacturing Organization) specializing in pharmaceutical and nutraceutical production, including ultra-pure Omega-3 concentrates.

The company operates internationally, with locations across Germany, Norway, the UK, the USA, Canada and Peru, and provides end-to-end solutions from development and custom synthesis through to finished dosage forms.

About the Project

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:

Assess the existing data landscape, integrations, data flows and Power BI dependencies

Reverse-engineer and document critical parts of the existing reporting and data environment

Identify key architectural, data-quality and integration gaps

Design the target bronze / silver / gold architecture and dimensional data models

Evaluate Microsoft Fabric, Azure Data Factory and Databricks and recommend the most suitable approach

Define how transformation logic currently embedded in Power BI can be progressively moved into a proper upstream data-processing layer

Stabilize critical parts of the existing reporting environment where required during the transition

Define the first implementation / PoC scope, with the group sales reporting flow expected to be one of the key end-to-end use cases

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.

About the Role

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.

Responsibilities

Assess and reverse-engineer the existing data and Power BI environment, including key data flows, dependencies and undocumented logic

Stabilize critical reporting and data p

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