Senior Azure Data Engineer | KD Pharma

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 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

Interview Steps

GT interview with Recruiter

Technical interview

Final interview

Offer