Sr. Scientific Data Visualization Engineer

Job Summary We are seeking a Scientific Data Visualization Engineer to build and deliver high-impact analytical tools for Translational Medicine scientists and Biologists. This is a hybrid role that combines front-end development with data stewardship. You will not only develop interactive applications using R Shiny and TIBCO Spotfire but also act as a data steward to ensure the underlying clinical and biomarker data is high-quality, FAIR, and automated. You will serve as the technical bridge between the raw data infrastructure and scientific end-users. Key Responsibilities Design and deploy a mix of custom R Shiny applications, framework-based tools (e.g., teal), and TIBCO Spotfire dashboards to answer complex scientific questions. Apply engineering best practices to automate dashboard updates and data pipelines; ensure visualizations are not static but are integrated into live data workflows. Partner with Data Engineers to define efficient schemas and views in the Data Lake (Azure Fabric) that optimize front-end performance and latency. Collaborate directly with biologists and translational scientists to translate abstract research questions into concrete visual queries (e.g., "Visualize responders vs. non-responders by gene expression"). Debug and refine dashboard code to handle large-scale multi-omics datasets without compromising user experience Partner with IT and Translational Sciences to define and implement data ingestion patterns for clinical biomarker datasets. Act as a data steward, ensuring data quality, metadata standards, and FAIR compliance for internal translational and clinical data assets. Support governance processes for data lifecycle management and documentation. Provide technical expertise on data engineering best practices and contribute to automation of ingestion workflows. Minimum Qualifications Education: Bachelor s or Master s degree in Data Science, Computer Science, Bioinformatics, or related field. Experience: 3-5 years of professional experience in data visualization, data engineering, computational biology, or software engineering. Core Technical Stack: Advanced proficiency in R (specifically Shiny, reactive programming, and package development) and TIBCO Spotfire. Data Engineering Fluency: Strong grasp of ETL concepts and automation; ability to write complex SQL and scripts to automate data preparation. Cloud Familiarity: Experience connecting R/Spotfire applications to cloud data sources (e.g., Azure Fabric, Databricks, AWS). Framework Flexibility: Experience with Shiny frameworks (such as teal or golem) and the ability to build custom solutions. Stewardship Mindset: Experience with data governance, metadata management, or FAIR data principles. Preferred Qualifications Familiarity with clinical data standards including SDTM, ADaM, and biomarker data formats (NGS variant results, flow cytometry, serum proteomics, gene expression profiling) Familiarity with Python for interoperability with backend engineering tasks. UX/UI Design: Knowledge of HTML/CSS/JavaScript to customize the look and feel of Shiny apps. Experience with database systems, APIs, and query optimization. Kaztronix is an equal opportunity employer and does not discriminate on the basis of race, color, national origin, sex, age, religion, disability, veteran status or any other consideration made unlawful by federal, state or local laws. In addition, all human resource actions in such areas as compensation, employee benefits, transfers, layoffs, training and development are to be administered objectively, without regard to race, color, religion, age, sex, national origin, disability, veteran status or any other consideration made unlawful by federal, state or local laws. By applying to the position, you acknowledge that your information will be used by Kaztronix in processing your application.