Data Engineer
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At CluePoints, we’re redefining how clinical trials are run. As the premier provider of Risk-Based Quality Management (RBQM) and Data Quality Oversight software, we harness advanced statistics, artificial intelligence, and machine learning to ensure the quality, accuracy, and integrity of clinical trial data, helping life sciences organisations bring safer, more effective treatments to patients faster.
We’re an ambitious, fast-growing technology scale-up with a dynamic and diverse international team. Collaboration, flexibility, and continuous learning are part of our DNA, and we are guided by our values of Care, Passion, and Smart Disruption.
The Role
The Data Engineer will build the pipelines, connectors and harmonized data snapshots at the heart of CluePoints’ new data layer. The layer brings together data from different studies and applications so that CluePoints’ consumer applications can use it, with auditability and regulatory compliance designed in from the start.
You will work with the platform architect and the AI Engineer to turn the architecture into reliable, production-grade data flows. Success looks like trustworthy, traceable, harmonized snapshots, delivered consistently across studies and data sources.
- Strong practical experience designing and running production-grade ingestion, transformation and orchestration pipelines.
- Advanced SQL and strong Python development skills.
- Hands-on experience with Databricks and/or Snowflake and modern lakehouse or warehouse architectures.
- Experience integrating data from databases, APIs and SaaS applications.
- Strong data modelling skills, including layered data architectures.
- Experience with Git-based development, code review, automated testing and CI/CD.
- Experience implementing data-quality controls, observability, monitoring and alerting.
- Understanding of data governance, metadata, lineage and traceability, and why they matter in regulated settings.
- Ability to reconcile data with differing structures and variables and to validate it against source systems.
- High autonomy and ownership from requirements through deployment, monitoring and maintenance.
Nice To Have
- Experience in clinical trials, life sciences or another regulated environment, or familiarity with clinical data standards (for example CDISC) and data-capture system exports.
- Experience designing metadata-driven pipelines or harmonizing data across heterogeneous sources.
- Experience with Delta Lake, Unity Catalog and Databricks Asset Bundles, and with Microsoft Azure.
- Experience building integrations into customer-managed data environments.
- Practical use of generative AI in data engineering workflows, and awareness of knowledge graphs and RAG.
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