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.

Data-Layer Pipelines & Harmonization

• Build and maintain production pipelines that bring data from different studies and applications into the data layer.
• Implement harmonization logic that reconciles studies with differing variables, including approaches driven by metadata from data-capture solutions.
• Produce harmonized data snapshots for CluePoints’ consumer applications, with clear refresh and versioning approaches.
• Validate new or changed data flows against source systems and business expectations.

Connectors & Integration

• Shape and build connector approaches for common data-capture systems.
• Support customer-specific Databricks and Snowflake environments with secure, repeatable integration patterns.
• Monitor and manage schema changes, missing data, duplication and inconsistencies between sources.
• Evaluate new data sources and determine the most appropriate extraction and integration approach.

Auditability, Quality & Compliance

• Ensure snapshots support auditability, traceability and regulatory compliance, including lineage, versioning and reproducibility.
• Implement automated data-quality controls covering completeness, accuracy, consistency, uniqueness and freshness.
• Set up monitoring and alerting for pipeline failures, unexpected schema changes and data-quality issues.
• Apply testing, validation and documentation standards appropriate to a regulated environment.

Platform Engineering & Collaboration

• Contribute to technology evaluation and architecture decisions with the platform architect.
• Establish version control, code review, automated testing, CI/CD and orchestration practices for the data layer.
• Monitor and optimize performance, resource usage and cost.
• Work with the AI Engineer to make well-described, harmonized data available to retrieval and AI components.
• Document critical solutions and share knowledge to avoid dependency on a single individual