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CluePoints

Cloud-based RBQM platform for clinical trials

Data Engineer

Data EngineeringConfirmed live in the last 24 hours
Full-timeBelgium

About the Job

Requirements

  • 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, application programming interfaces, and software-as-a-service applications.
  • Strong data modelling skills, including layered data architectures.
  • Experience with Git-based development, code review, automated testing, and continuous integration and continuous delivery.
  • 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 validate it against source systems.
  • High autonomy and ownership from requirements through deployment, monitoring, and maintenance.

Responsibilities

  • 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 consumer applications, with clear refresh and versioning approaches.
  • Validate new or changed data flows against source systems and business expectations.
  • 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.
  • 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.
  • Contribute to technology evaluation and architecture decisions with the platform architect.
  • Establish version control, code review, automated testing, continuous integration and continuous delivery, 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.

Desired Qualifications

  • Experience in clinical trials, life sciences, or another regulated environment, or familiarity with clinical data standards such as CDISC and data-capture system exports.
  • Experience designing metadata-driven pipelines or harmonizing data across heterogeneous sources.
  • Experience with Delta Lake, Unity Catalog, Databricks Asset Bundles, and 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 retrieval-augmented generation.

About the Company

CluePoints

Cloud-based RBQM platform for clinical trials

CluePoints provides risk-based quality management and data quality oversight software for clinical trials. Its cloud-based platform analyzes trial data using advanced statistics, machine learning, and AI to identify risks and monitor data quality from initial risk assessment through issue tracking. Key products—Site Profile and Oversight Tool (SPOT), Intelligent Medical Coding (IMC), Intelligent Query Detection (IQD), and Medical & Safety Review (MSR)—automate data discrepancy detection, improve medical review efficiency, reduce manual coding, and enable adaptive site monitoring. The company differentiates itself with an end-to-end, multi-product platform (expanded in 2024) that combines RBQM, data analytics, and regulatory-aligned monitoring to help pharmaceutical/biotech companies and CROs de-risk trials and ensure data integrity and patient safety. Its goal is to help customers meet regulatory guidance from bodies like the FDA and EMA while improving trial quality and efficiency.

Company size
201-500
Company stage
Acquired
Total funding
$6.6M
Headquarters
Ottignies-Louvain-la-Neuve, Belgium
Founded
2012

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