Senior Azure Data Engineer

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 Azure Data Engineer with architecture exposure, interested in assessing, designing, and potentially building a modern data platform to support Finance, Operations/Supply Chain, and Quality/Manufacturing functions.

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 roughly nine source systems across Business Central, legacy NAV, QuickBooks and other integrations, with reporting currently relying on a mix of direct ERP/SQL connections and Power BI.

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 and data flows
• Identify key architectural, data-quality and integration gaps
• Design the target lakehouse / medallion architecture
• Evaluate Microsoft Fabric, Azure Data Factory and Databricks and recommend the most suitable approach
• Define the first implementation / PoC scope and 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 hands-on Senior Data Engineer role with strong architecture exposure .

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.

During the initial six weeks, the role will combine technical discovery, architecture design and hands-on prototyping . You will help assess the existing environment, define the target approach, make technology recommendations, and contribute to building and validating an initial PoC that demonstrates the proposed solution.

If the project proceeds into implementation, the role is expected to become considerably more hands-on and may transition into a long-term, full-time engagement .

Responsibilities

• Assess the current data estate, including source systems, integrations, ETL/data flows, Power BI dependencies and existing Fabric components
• Understand and document existing data flows and technical dependencies, helping preserve critical knowledge of the current environment
• Identify data-quality, integration, scalability and maintainability issues
• Contribute to the design of the target bronze / silver / gold lakehouse architecture
• Define scalable ingestion and transformation patterns for multiple ERP and other enterprise data sources
• Evaluate Microsoft Fabric, Azure Data Factory and Databricks and contribute to the platform recommendation
• Assess technical trade-offs including platform fit, maintainability, performance and operating cost
• Define the first end-to-end PoC together with its scope and technical success criteria
• Contribute to implementation estimates, sequencing and the wider technical roadmap
• Collaborate closely with the Solution Architect and client stakeholders throughout Discovery
• Potentially transition into hands-on implementation of the platform following client approval

Essential knowledge, skills & experience

• 6+ years of experience in data engineering, BI or enterprise data platforms
• Strong hands-on experience with the Microsoft Azure data ecosystem
• Strong experience with Azure Data Factory and modern data lake / lakehouse architectures
• Practical commercial experience with Microsoft Fabric , including Lakehouse and/or Warehouse components
• Advanced SQL / T-SQL
• Experience with Python and/or PySpark
• Strong understanding of ETL/ELT, data integration and medallion architecture patterns
• Experience designing solutions that integrate multiple enterprise source systems
• Good understanding of Power BI, dimensional modelling and semantic-layer concepts
• Experience with Git, Azure DevOps and CI/CD practices in data-platform environments
• Experience contributing to technical discovery, architecture design, technology selection, estimation or implementation planning
• Ability to assess existing systems, identify architectural issues and recommend pragmatic solutions rather than simply implement predefined requirements
• Strong English and confidence communicating with both technical and business stakeholders

Nice-to-have

• Experience evaluating Fabric vs. Databricks and/or other Azure data-platform approaches
• Fabric capacity monitoring, SKU sizing or cost-optimisation experience
• Experience building or evaluating cloud/data-platform consumption and operating-cost models
• Metadata-driven ETL framework experience
• Multi-ERP integration experience, particularly with Business Central, NAV, QuickBooks or SAP
• Experience with Purview, Databricks or Synapse
• Strong Power BI experience including DAX or Tabular modelling
• Experience within pharmaceutical, manufacturing or other regulated environments
• Knowledge of GxP environments — domain knowledge can be learned