Principal Data Engineer

Principal Data Engineer

Full-time, remote from anywhere in the UK.

The Principal will be responsible for supporting complex or leading singular projects related to data engineering requirements and initiatives across Research and Development. The Principal will support data projects from across the business including Clinical, Pre-Clinical, Non-Clinical, Chemistry, RWD and Omics.

• Support the design, development and maintenance of data pipelines for processing Research and Development data from diverse sources (Clinical Trials, Medical Devices, Pre-Clinical, Omics, Real World Data) utilizing the AWS technology platform.
• Create and optimize ETL/ELT processes for structured and unstructured data using Python, R, SQL, AWS services and other tools.
• Build and maintain data repositories using AWS S3 and FSx technologies. Establish data warehousing solutions using Amazon Redshift.
• Build and maintain standard data models.
• Develop data quality frameworks, validation processes and KPIs to ensure accuracy and consistency of data pipelines.
• Implement data versioning and lineage tracking to support data traceability, regulatory compliance and audit requirements.
• Create and maintain documentation for data processes, architectures, and workflows.
• Implement modern software development best practices (e.g. Code Versioning, DevOps, CD/CI).
• Maintain compliance with data privacy regulations such as HIPAA, GDP.
• May be required to develop, deliver or support data literacy training across R&D.

• Strong knowledge of data engineering tools such as Python, R and SQL for data processing.
• Strong proficiency with AWS services particularly S3, Redshift, FSx, Glue, Lambda.
• Strong proficiency with relational databases.
• Strong background in data modeling and database design.
• Familiarity with unstructured database technologies (e.g. NoSQL) and other database types (e.g. Graph).
• Familiarity with Containerization such as Docker and EKS/Kubernetes.
• Familiarity with one or more RnD research process and associated regulatory requirements.
• Exposure to healthcare data standards (CDISC, HL7, FHIR, SNOMED CT, OMOP, DICOM).
• Exposure to big data technologies and handling.
• Knowledge of machine learning operations (MLOps) and model deployment.
• Strong problem-solving and analytical abilities.
• Excellent communication and collaboration skills.
• Experience working in an Agile development environment.

• Bachelor's Degree in Computer Science, Statistics, Mathematics, Life Sciences, or other relevant scientific fields; Master's Degree preferred.
• 3-5 years of experience in data engineering, with at least 1.5 years focusing on healthcare, research or clinical related data.