Data Engineer

• Design, develop, automate, and maintain robust data pipelines
• Build, implement, and automate data ingestion pipelines integrating structured, semi-structured, and unstructured data sources
• Apply business logic and transformations to ingested data
• Develop and maintain data processing solutions using Azure Databricks, Azure Data Factory, SQL, Python, and PySpark
• Apply data quality practices and frameworks to minimize anomalies and produce reliable downstream data
• Implement error handling, exception management, monitoring, and auditing mechanisms
• Maintain end-to-end data traceability and support data lineage
• Design and execute end-to-end tests across varied data scenarios
• Ensure accurate and reliable data flow from source to target
• Collaborate with data privacy officers, business analysts, quality assurance teams, cross-functional teams, and pharmaceutical domain experts
• Support data architecture for advanced analytics and business intelligence across pharmaceutical projects
• Contribute to meaningful consulting and technology solutions for life sciences clients

Data Engineer – DInA

• Engage directly with customers onsite, build strong relationships, understand scientific data challenges and requirements, and accelerate solutions.
• Design and implement extensible, reusable data models that capture and organize scientific data for scalability and future adaptability.
• Translate scientific data workflows into robust solutions using the Tetra Data Platform.
• Own, scope, prototype, and implement data model designs in tabular and JSON formats.
• Develop Python-based parsers.
• Integrate lab software such as ELN/LIMS via APIs.
• Develop data visualizations and applications in Python using frameworks such as Streamlit and tools such as holoviews and Plotly.
• Collaborate with Scientific Business Analysts, customer scientists, and applied AI engineers to develop and deploy ML, AI, mechanistic, statistical, and hybrid models.
• Programmatically interrogate proprietary instrument output files.
• Iterate with scientific end users and technical stakeholders through demos and meetings to drive solution development and adoption.
• Communicate implementation progress and deliver demos to customer stakeholders.
• Collaborate with the product team to build and prioritize the roadmap based on customer pain points.
• Learn new technologies and troubleshoot use cases.

Data Engineer jobs in Denmark

Data Engineer – DInA

Scientific Data Architect