Perform data analysis across all levels of complexity-from initial data preparation to applying AI/ML techniques, including Large Language Models (LLMs).
Build KNIME workflows to automate repetitive data manipulation and transformation tasks.
Automatically generate workflow components using KNIME's generative AI assistant and seek recommendations when facing analytical challenges.
Access, clean, and blend data from multiple sources, including desktop files, relational databases, data warehouses, and cloud systems.
Explore and analyze datasets using interactive data views and various charting/visualization options available in KNIME.
Develop reusable data processes, analytical models, and scripts across different teams and departments.
Collaborate with colleagues and business teams by sharing, reviewing, and enhancing visual workflows.
Create and maintain a knowledge base containing reusable workflows, best practices, and starter templates for team learning and onboarding.
Document workflow logic, business requirements, and solution designs to support cross-team alignment.
Deploy KNIME workflows as data applications or REST APIs to distribute insights across the company.
Automate scheduled reports and analytics delivery to internal stakeholders.
Implement scalable solutions that support a large number of users and high workflow volume within a unified KNIME environment.
Ensure adherence to data governance, quality standards, and process consistency across analytical projects.
Work closely with IT, business users, and data teams to ensure smooth deployment and usage of KNIME applications and solutions.
Requirements
Minimum Diploma or Bachelor's degree in Computer Science, Information Systems, Statistics, Business Analytics, or a related field.
1 to 2 years of hands-on experience or strong working knowledge in KNIME Analytics Platform.
Basic understanding of data analysis, ETL processes, data modeling, or workflow automation.
Familiarity with SQL, Python, or other scripting languages is an advantage (not mandatory).
Strong analytical thinking, problem-solving skills, and attention to detail.
Ability to communicate effectively with both technical and non-technical stakeholders.
Willingness to learn new technologies, tools, and data automation techniques.
