Data Engineer
Data Engineer (Reporting & Analytics)
Our client is a global science and healthcare technology organisation whose work directly supports critical medical, diagnostic, and life‑sciences outcomes. With a strong culture of continuous improvement, they turn data and innovation into real‑world impact in highly regulated environments.
The organisation employs tens of thousands of people globally, operating across multiple business units within life sciences, diagnostics, and biotechnology.
They are looking for a Data Engineer (Reporting & Analytics) to support the ongoing growth of their enterprise data and analytics capabilities.
Role Overview
The Data Engineer (Reporting & Analytics) is responsible for building and maintaining high‑quality, analytics‑ready data assets that support enterprise‑wide reporting and business intelligence across multiple operating units.
This role focuses on developing scalable, reliable data pipelines, enabling trusted reporting, and supporting self‑service analytics platforms in close partnership with analytics, business intelligence, and stakeholder teams.
The role sits within a centralised Data Engineering function as part of a broader Core Technology & Planning organisation.
Key Responsibilities
In this role, you will:
- Build and maintain analytics‑ready datasets optimised for dashboards and enterprise reporting
- Develop and support ETL / ELT pipelines that extract, transform, and load data from multiple internal and external sources into lakehouse and reporting layers
- Enable self‑service analytics by contributing to semantic models, curated datasets, data catalogues, and APIs
- Partner closely with analytics and business teams to translate reporting requirements into accurate, reusable data models and metrics
- Ensure data accuracy, timeliness, traceability, and consistency across reporting platforms
- Implement data quality checks, testing frameworks, and documentation to support reliable analytics in regulated environments
- Contribute to CI/CD pipelines, version control, and deployment processes for data solutions
Essential Requirements
- 4-7 years' experience in data engineering, analytics engineering, business intelligence, or closely related roles
- Strong proficiency in PySpark for data transformation and processing
- Hands‑on experience building data solutions on Fabric or Databricks lakehouse architectures, including medallion (bronze / silver / gold) patterns
- Practical experience with Power BI, including datasets, semantic models, DirectQuery or Import modes, and security concepts (e.g. RLS)
- Experience designing and maintaining cloud‑based data pipelines in Azure and/or AWS environments
- Solid understanding of SQL, data modelling principles, and reporting optimisation
- Familiarity with CI/CD and DevOps practices within data engineering workflows
Highly Desirable Experience
- Prior experience working in medical, life sciences, diagnostics, pharmaceutical, or healthcare environments, particularly where data quality, auditability, and regulatory compliance are critical
- Exposure to modern data warehouses such as Snowflake, BigQuery, or Redshift
- Experience with data governance, metadata management, lineage, or data quality frameworks
- Familiarity with multi‑cloud data ecosystems (Azure, AWS, and/or GCP)
- Experience supporting enterprise‑grade or finance‑grade reporting and analytics
FAQs
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