R&D Data Engineer & Integrations specialist

Key Responsibilities 1. R&D Data Integration & Transformation Design and implement data cleaning, validation, and transformation workflows using Python, SQL, and REST APIs. Integrate experimental, master, and transactional data into the R&D data management platform. Develop reusable ETL/ELT pipelines and automation workflows to improve data consistency, reliability, and traceability. Support data migration, harmonization, and enrichment activities across multiple scientific and business systems. Collaborate with business stakeholders to translate data requirements into scalable integration solutions. 2. Laboratory Device & Instrument Integration Design and implement integrations between laboratory instruments and the R&D data management platform. Automate the acquisition, processing, and ingestion of analytical and experimental data from laboratory equipment. Develop and maintain interfaces using APIs, file-based integrations, and standard laboratory communication protocols. Monitor and troubleshoot instrument data flows to ensure reliable and timely availability of scientific data. Support onboarding of new laboratory devices and standardization of data exchange processes. 3. Enterprise Application Integration Integrate the R&D platform with enterprise applications such as ELN, LIMS, ERP, PLM, MDM, and document management systems. Design and implement API-based, event-driven, and batch integration solutions for seamless data exchange. Develop and maintain middleware services, synchronization workflows, and integration components. Ensure secure, reliable, and auditable data transfer between scientific and enterprise systems. Create technical specifications and integration documentation to support long-term maintainability. 4. Data Quality Monitoring & Governance Design and implement automated data quality monitoring solutions for master data and ELN transactional data. Define and maintain data quality rules covering completeness, consistency, validity, uniqueness, integrity, and business-specific requirements. Develop dashboards, KPIs, and alerting mechanisms to proactively monitor data quality across R&D processes. Perform root-cause analysis of data quality issues and implement corrective and preventive measures. Support data governance initiatives by establishing standards, controls, and best practices for scientific data management. 5. Scientific Application Deployment & Integration Develop and deploy lightweight scientific web applications using Streamlit and Python to enable researchers to access analytical tools and workflows. Support the deployment of scientific and cheminformatics applications to AWS and other cloud environments, ensuring security, scalability, and operational stability. Integrate deployed applications with ELN, LIMS, and other R&D systems through APIs and standardized integration frameworks. Collaborate with data scientists and cheminformatics experts to operationalize existing models, workflows, and scientific tools, focusing on deployment and integration rather than algorithm development. Implement monitoring, logging, access management, and deployment automation to support reliable application operations. Create reusable deployment patterns, infrastructure templates, and technical documentation to accelerate future scientific application delivery.