Clinical Data Engineer

Job Title: Clinical Data Engineer

Contract Type:

12-Month Fixed-Term Contract Location:

Ireland (Hybrid/Onsite depending on business needs) Working Hours:

40 hours per week Start Date:

Immediate requirement

Role Overview: This organization is executing a multi-year

Clinical Supply Chain Data & Analytics Strategy Roadmap

designed to transform how clinical trial and supply chain data is collected, governed, and analyzed. The program focuses on building scalable data platforms, improving data quality and compliance, and enabling advanced analytics to support operational and strategic decision-making across clinical operations.

The

Clinical Data Engineer

will be a key contributor to this initiative, supporting the design and implementation of robust data pipelines and analytics-ready datasets. This role is well suited to an early-career data engineer with a strong technical foundation, exposure to regulated data environments, and a strong interest in clinical trials, life sciences, or healthcare data. You will work closely with data architects, analysts, and cross-functional stakeholders to convert raw clinical and operational data into trusted, high-quality datasets that enable reporting, dashboards, and advanced analytical use cases.

Key Responsibilities:

Data Engineering & Pipeline Development Design, build, and maintain automated data pipelines to ingest, transform, and load clinical trial and supply chain data from multiple systems. Implement ETL/ELT processes using modern data transformation tools such as

dbt

or equivalent frameworks. Ensure data pipelines are scalable, reliable, maintainable, and meet performance and availability requirements. Support onboarding of new data sources and enhancements to existing data integrations.

Data Modelling & Architecture Design and implement data models (e.g., dimensional, analytical schemas) optimized for reporting and analytics. Transform raw and semi-structured data into standardized, analytics-ready tables and views. Align data structures with enterprise data architecture and long-term analytics strategy.

Data Quality, Governance & Compliance Develop and execute data validation, reconciliation, and cleansing processes to ensure high data quality. Apply data governance standards to maintain data integrity, traceability, and regulatory compliance (e.g.,

GxP ,

GDPR ). Contribute to documentation, metadata management, and audit-ready data assets.

Analytics & Business Support Analyze clinical and operational datasets to identify trends, anomalies, and insights. Support the creation of dashboards, reports, and visualizations for operational and leadership stakeholders. Collaborate with analytics and data science teams by delivering reliable datasets for downstream use.

Delivery & Independent Ownership Manage assigned tasks and workstreams with minimal supervision, ensuring timely and high-quality delivery. Proactively identify process improvements, automation opportunities, and data platform enhancements.

Must Have Requirements:

Candidates

must

demonstrate the following: Bachelor’s degree in

Data Science, Computer Science, Statistics , or a related field. 0–3 years of hands-on experience

in data engineering, data analytics, data curation, or a closely related role. Strong proficiency in

SQL , including writing complex queries and transformations. Practical experience with

ETL/ELT pipelines , data ingestion, and data transformation processes. Working knowledge of

Python and/or R

for data manipulation and analysis. Experience working with structured and semi-structured datasets. Understanding of

data modeling concepts

(e.g., fact/dimension tables, analytical schemas). Familiarity with

data quality management

and validation techniques. Awareness of data governance and compliance principles in regulated environments (e.g., GDPR, GxP concepts). Experience using

data visualization or BI tools

such as Tableau or Power BI. Strong analytical thinking, attention to detail, and problem-solving skills. Ability to work independently, manage multiple priorities, and meet deadlines. Strong written and verbal communication skills, with the ability to collaborate across technical and non-technical teams.

Nice to Have Requirements:

The following are

desirable but not essential : Previous experience within

clinical trials, life sciences, healthcare, or pharmaceutical

environments. Hands-on experience with

dbt

in a production or project-based setting. Certification in

Data Analytics ,

Data Engineering , or a related discipline. Certification or experience with specific

ETL tools or platforms . Familiarity with

clinical trial systems

(e.g., CTMS, IRT, EDC) and associated data structures. Experience working in

Agile or Scrum

delivery environments;

JIRA certification

is an advantage. Exposure to

cloud-based data platforms

and modern analytics stacks. Basic experience or academic exposure to

machine learning or predictive modeling . Experience contributing to large-scale, multi-year data transformation programs.