Data Architect Scientist

Freelance Data Engineer / Data Architect



Pharmaceutical Research & Clinical Trials

Engagement type: Long-term freelance contract, outside IR35, B2B

Sector: Pharmaceutical research, clinical trials, life sciences

Location: Remote / hybrid, subject to client requirements

Rate: Competitive day rate, dependent on experience

We are seeking an experienced freelance Data Engineer / Data Architect to design, build, and deliver scalable, secure, and well-governed data solutions for pharmaceutical research and clinical-trial environments.

This is a hands-on data engineering and architecture role, with significant responsibility for analytical solutions, data quality, and reporting. The successful contractor will work closely with business stakeholders, clinical and research teams, analysts, and technical delivery teams to translate complex requirements into reliable, maintainable, and audit-ready data platforms.

The client is specifically seeking a contractor with a strong traditional data-engineering background: substantial hands-on coding experience, advanced SQL capability, and proven ability to design dependable end-to-end data solutions. While generative-AI tooling may support delivery, it is not a substitute for strong engineering fundamentals.

About the role

You will design and implement end-to-end data solutions covering ingestion, transformation, storage, modelling, governance, quality assurance, reporting, and self-service analytics. The role will involve working with internal pharmaceutical and clinical-trial data, alongside relevant external, third-party, and open-source datasets.

This is primarily a Data Engineer / Data Architect position. Although data-science capability may be beneficial, the core focus is on building robust data infrastructure, pipelines, data models, integrations, and dashboards that support pharmaceutical research and clinical-trial use cases.

You will take ownership of solutions from discovery and architecture through development, testing, deployment, documentation, and stakeholder handover.

Key responsibilities

• Act as a senior Data Engineer and Data Architect across pharmaceutical research and clinical-trial data initiatives.
• Engage with stakeholders to understand business, clinical, analytical, operational, and reporting requirements.
• Design end-to-end data architectures, including ingestion, raw and curated data zones, transformation layers, data models, and reporting/consumption layers.
• Build scalable ETL/ELT pipelines to ingest, cleanse, standardise, validate, transform, and integrate data from internal systems, APIs, secure file transfers, third-party sources, and open or public data sources.
• Work with pharmaceutical research, clinical-trial, laboratory, safety, operational, and related life-sciences datasets where applicable.
• Design relational and analytical data models, including source-to-target mappings, dimensional models, data-vault patterns where appropriate, and consumption-layer models for reporting.
• Develop and optimise complex SQL queries, stored procedures, views, indexes, partitions, and other database objects.
• Develop production-quality code in both R and Python.
• Use R for data manipulation, exploratory analysis, statistical validation, data profiling, reproducible research workflows, automation, and clinical or research reporting.
• Use Python for scalable data engineering, data pipelines, API and database integrations, automation, data-quality checks, and cloud-platform workflows.
• Develop reusable, well-documented code libraries and analytical components that support clinical-trial and pharmaceutical-research use cases.
• Ensure analytical outputs are reproducible, version-controlled, tested, traceable, and auditable from source data through to dashboard or report.
• Connect to and deliver solutions within Microsoft Azure environments.
• Design and implement solutions using platforms such as Azure Data Factory, Azure Synapse, Azure Data Lake Storage, Databricks, Snowflake, or Microsoft Fabric, depending on the client's technology landscape.
• Build and maintain high-quality dashboards and reporting solutions in Tableau and/or Power BI.
• Ensure data quality, lineage, traceability, auditability, security, performance, and maintainability across delivered solutions.
• Produce clear technical documentation, including architecture diagrams, data-flow diagrams, data dictionaries, source-to-target mappings, design decisions, and deployment guidance.
• Clearly explain technical designs, architecture choices, data flows, and delivery progress to both technical and non-technical stakeholders.
• Support testing, deployment, troubleshooting, performance tuning, and continuous improvement of data solutions.
• Work in line with relevant data-governance, privacy, security, and regulated life-sciences requirements.
  
  

Essential skills and experience

• Significant experience as a Data Engineer, Senior Data Engineer, Data Architect, or equivalent, ideally within pharmaceutical, biotech, life sciences, clinical research, healthcare, or another regulated industry.
• Strong, demonstrable hands-on coding experience in both R and Python. This is a core requirement, not a nice-to-have.
• The successful contractor must be equally comfortable using R for data manipulation, statistical analysis, reproducible analysis, automation, and clinical/research reporting, and Python for data engineering, integration, transformation, and production pipelines.
• Strong SQL skills, including complex querying, data validation, stored procedures, performance tuning, and database-object development.
• Proven experience designing and delivering ETL/ELT pipelines and data-integration solutions.
• Strong understanding of data architecture, including data lakes, warehouses, lakehouse patterns, medallion architecture, dimensional modelling, and reporting layers.
• Deep knowledge of database design, data modelling, normalisation, dimensional modelling, indexing, partitioning, and query optimisation.
• Hands-on experience connecting to and working within Microsoft Azure.
• Experience building dashboards and reports using Tableau and/or Power BI.
• Ability to identify, assess, and integrate relevant open-source or external datasets alongside internal client data.
• Excellent stakeholder-management and communication skills, with the ability to explain architecture, data lineage, design decisions, and delivery progress clearly.
• Strong documentation skills and a structured, quality-focused approach to delivery.
• Experience working independently as a freelance contractor within a B2B engagement.
  
  

Essential technical profile

R and Python: The successful contractor must have strong, hands-on experience in both R and Python. This is not a Python-only role. R is a core requirement for data analysis, statistical validation, reproducible research workflows, and clinical/pharmaceutical reporting. Python is required for scalable data engineering, integrations, transformations, automation, and production data pipelines.

The ideal candidate will be able to explain clearly when and why they would use each language within an end-to-end data solution.

SQL: Advanced SQL capability is essential, including complex querying, data validation, performance tuning, stored procedures, and database-object development.

Data architecture: Deep understanding of modern data architecture, including data lakes, warehouses, lakehouse patterns, dimensional/data-vault modelling, and reporting layers.

Azure: Hands-on experience designing and delivering secure, scalable cloud data solutions within Microsoft Azure.

BI and reporting: Proven experience creating dashboards and reports in Tableau and/or Power BI.

Desirable skills and experience

• Hands-on experience with one or more of the following: Databricks, Snowflake, or Microsoft Fabric.
• Experience with Azure services such as Azure Data Factory, Azure Synapse, Azure Data Lake Storage Gen2, Azure Key Vault, Azure DevOps, and related cloud-native tooling.
• Familiarity with clinical-trial data standards and concepts, such as CDISC, SDTM, ADaM, EDC exports, laboratory data, adverse-event data, or clinical operational data.
• Experience with R Shiny, R Markdown, or other reproducible R-based analytical reporting tools.
• Knowledge of data governance, metadata management, data lineage, master data management, and data-quality frameworks.
• Experience with Git, CI/CD, automated testing, environment promotion, and DevOps/DataOps practices.
• Experience integrating data from APIs, secure file transfers, third-party vendors, and external research sources.
• Awareness of GxP, 21 CFR Part 11, GDPR, HIPAA, or other regulatory and compliance considerations relevant to pharmaceutical data platforms.
• Experience working with large, complex, multi-source datasets in a regulated or audit-ready environment.
  
  

What makes a strong candidate

The ideal contractor is a highly capable, pragmatic Data Engineer and Data Architect who can take ownership of a problem from initial discovery through to final delivery. They should be comfortable working with complex, messy, and heterogeneous data; designing a sensible target architecture; building reliable pipelines; and creating trusted dashboards and analytical outputs.

The successful candidate will be able to explain the full process to the client: the business requirement, source systems, ingestion approach, transformations, data model, quality controls, architecture, and final reporting solution. They should be able to justify design decisions and communicate trade-offs in clear, business-friendly language.

This is not solely an AI or machine-learning modelling role. The client is looking for someone with a strong traditional data-engineering foundation, excellent R and Python skills, and the ability to build trusted data products and dashboards for pharmaceutical research and clinical-trial use.

Experience with Databricks, Snowflake, or Microsoft Fabric will be viewed very positively, particularly where combined with Azure, SQL, R, Python, data modelling, and dashboarding expertise.

Deliverables

Depending on the scope of the engagement, deliverables may include:

• Data architecture and solution-design documentation.
• End-to-end data pipelines and integration components.
• Data models, schemas, and source-to-target mapping specifications.
• SQL, R, and Python-based transformation, validation, analysis, and automation code.
• Azure-based data-platform components and configurations.
• Tableau or Power BI dashboards and reporting assets.
• Data-quality, reconciliation, lineage, and auditability controls.
• Technical documentation, deployment guidance, and stakeholder walkthroughs.
  
  

Engagement details

• Contract type: Freelance / B2B, outside IR35.
• Duration: Long-term.
• Working pattern: To be confirmed with the client.
• Sector: Pharmaceutical research, clinical trials, life sciences, or healthcare.
• Core profile: Data Engineer / Data Architect with strong R, Python, SQL, Azure, BI, and clinical-data experience.
  
  

How to apply

Please submit an up-to-date CV highlighting your pharmaceutical, clinical-trial, or life-sciences data experience. Please clearly evidence your experience with:

• R and Python.
• SQL and database design.
• Azure and cloud data platforms.
• Data architecture, ETL/ELT, and data modelling.
• Tableau and/or Power BI.
• Databricks, Snowflake, or Microsoft Fabric, where applicable.
• Clinical, pharmaceutical, life-sciences, or regulated-industry data environments.

Candidates should be prepared to describe their approach to architecture, data modelling, pipeline design, data quality, stakeholder communication, and end-to-end solution delivery.

Darwin Recruitment is acting as an Employment Business in relation to this vacancy.