Lead Full Stack Data Engineer
ConvaTec
The Lead Full Stack Data Engineer owns Data products across the full lifecycle: from data ingestion and transformation through to semantic modelling, reporting, documentation, and user adoption. Working closely with commercial, operations, finance, and clinical stakeholders, the role translates complex and often ambiguous business requirements into clear, reliable, and scalable Analytics solutions built on Microsoft Fabric and Power BI.
This position requires strong technical capability across data engineering and Analytics, a structured analytical mindset, and the ability to collaborate effectively with both technical and non-technical stakeholders.
About Role
1. End-to-End BI Solution Development
Design, develop, and implement full-stack Data solutions from data ingestion through to user-facing reports and apps.
Take ambiguous business questions and translate them into well-defined data products, including data models, semantic layers, dashboards, and supporting documentation.
Manage delivery from discovery and prototyping through validation and production support.
Ensure data accuracy, integrity, and reliability across all Data solutions.
2. Data Pipelines, Modelling, and ETL
Develop and maintain ingestion and transformation pipelines using Microsoft Fabric (Data Factory pipelines, dataflows Gen2, copy activities).
Land, organise, and structure data using Fabric Lakehouse and/or Data Warehouse patterns.
Implement layered data architectures (e.g. bronze / silver / gold) to ensure modular, reusable, and testable transformations.
Set ETL/ELT standards, performance optimisation, and scalable design.
Apply data quality checks, validation rules, and monitoring to proactively identify issues.
Build and govern the data foundations that AI and ML depend on – clean, well-modelled, well-documented datasets and feature-ready
CI/CD for the data platform (GitHub) - branching, automated testing deployment pipelines and raise the bar on engineering discipline
3. Semantic Models, Reporting, and Visualisation
Design dimensional and star schemas optimised for Analytics use cases (facts, dimensions, SCDs, conformed dimensions).
Build and maintain Power BI semantic models (datasets) with clear, consistent business logic using DAX measures and calculated columns.
Develop high-quality Power BI reports, dashboards, and apps that are intuitive, performant, and aligned to real business workflows.
Implement navigation, drill-through, parameterised views, and security (row-level and object-level) as required.
Optimise models and reports for performance, maintainability, and scalability.
4. Data Analysis and Insights
Perform data analysis to identify trends, patterns, and opportunities that support decision-making.
Collaborate with stakeholders to understand analytical needs and deliver actionable insights.
Support advanced analytics use cases where appropriate, including predictive or statistical models in partnership with other specialists.
Enable AI-assisted analytics (e.g. Power BI Copilot) by ensuring semantic models are well-structured, described, and governed enough for AI to query reliably.
5. Data Governance, Documentation, and Support
Ensure Data solutions comply with data governance policies, security standards, and regulatory requirements.
Contribute to and follow standards for naming, workspace structure, environments, and deployment flows in Fabric.
Document Data products clearly, including purpose, data sources, refresh cadence, ownership, lineage, and known limitations.
Contribute to shared libraries of reusable calculations, templates, and visual standards.
Provide structured handovers, user support, and occasional training or knowledge-sharing sessions.
6. Collaboration and Continuous Improvement
Work closely with business stakeholders to prioritise work and agree success measures for Data products.
Collaborate with wider data engineering fuction, architecture, governance, and security teams to align on standards and patterns.
Participate in peer reviews of SQL, Python, DAX, data models, and report designs.
Support continuous improvement of Data delivery practices, promoting full-stack ownership and reduced hand-offs.
7. Leadership
Line manager data engineers (Including activities such as setting objectives, carrying out one to ones, performance reviews and development)
About You
Strong proficiency in SQL/Python for data transformation, modelling, and performance tuning.
Hands-on experience with Microsoft Fabric components, including:
Data Factory (pipelines, dataflows Gen2)
Lakehouse and/or Data Warehouse
Power BI datasets, reports, and apps
Strong Power BI skills, including DAX, semantic modelling, and performance optimisation.
Solid understanding of data warehousing and dimensional modelling concepts.
Experience implementing BI security models (e.g. RLS).
Familiarity with Git-based source control and CI/CD practices for BI and analytics assets.
Experience working with enterprise source systems (e.g. ERP, CRM, finance systems).
Strong analytical and problem-solving skills, with the ability to translate business needs into technical sol