Senior Data Engineer

• Design, build and maintain enterprise data models and data assets on Microsoft Azure to support analytics, reporting, ML/AI and data products.
• Translate business requirements into robust logical and physical data models, ensuring alignment with data architecture, governance, security and performance standards.
• Enable reusable, well-documented data structures that accelerate insight delivery and reduce technical debt.

Key responsibilities

• • Develop conceptual, logical and physical data models for transactional, analytical and semantic layers (including star/snowflake schemas, data vault or other patterns as appropriate).
  • Define entities, attributes, relationships, keys, constraints and data types in line with client standards.
  • Drive consistent modeling across projects to support integration, reuse and scale.
  • Implement models using Azure services: Azure SQL Database / Azure Synapse / Azure Data Lake (Gen2) / Synapse dedicated SQL pools / Azure Databricks / Cosmos DB as applicable.
  • Work with engineers to implement ELT/ETL pipelines (Azure Data Factory / Synapse Pipelines / Databricks notebooks) that load and transform data into modelled structures.
  • Engage with business stakeholders, data engineers, data scientists, analytics teams and product owners to gather and validate data requirements.
  • Partner with enterprise architecture, cloud engineering and security teams to ensure solutions meet standards and constraints.
  • Ensure data modelling meets data privacy, classification and security requirements; support access control design (RBAC, row-level security).
  • Recommend partitioning, indexing, materialised views / cached layers and compute sizing to balance performance and cost.

Required qualifications & experience

• Degree in Computer Science, Information Systems, Data Science, Engineering or a related discipline (or equivalent practical experience).
• Proven hands-on experience designing and implementing data models for analytics and reporting on Microsoft Azure in enterprise environments.
• Experience with one or more of: Azure Synapse Analytics, Azure SQL Database, Azure Data Lake Gen2, Azure Databricks, Cosmos DB.
• Strong knowledge of data modelling techniques (3NF, dimensional modelling, data vault) and experience selecting appropriate patterns.
• Practical experience with data ingestion and transformation tools such as Azure Data Factory, Synapse Pipelines or Databricks.
• Familiarity with metadata management tools (Azure Purview or equivalent), data lineage and data quality frameworks.
• Strong SQL skills; experience optimising SQL for analytical workloads.
• Understanding of data security, privacy and regulatory requirements relevant to pharma/healthcare desirable.

Preferred skills and attributes

• Azure certifications (e.g., Microsoft Certified: Azure Data Engineer Associate, Azure Solutions Architect) desirable.
• Knowledge of big data processing (Spark), Python/Scala for data engineering tasks.
• Experience working in regulated industries (pharmaceuticals, healthcare) and with clinical/trial data models desirable.
• Strong problem solving, attention to detail and pragmatic approach to delivery in agile teams.