G2i Inc. - Data Visualization Engineer
Requirements
• Professional experience building analytics platforms, dashboards, or data visualization products • Strong proficiency with Python for data processing, analysis, or backend development • Experience with TypeScript and modern web application development • Practical knowledge of data visualization principles and libraries or BI platforms • Experience working with enterprise data sources, APIs, and reporting workflows • Familiarity with the Microsoft enterprise ecosystem, such as Power BI, Excel, Azure, Microsoft 365, or related tools • Familiarity with Google Workspace and its data or productivity tools • Experience using AI coding assistants or generative AI tools in day-to-day workflows • Ability to work independently, make sound implementation decisions, and deliver production-ready solutions • Strong written and verbal English communication skills • Availability to collaborate with teams working primarily across North American time zones • Experience with Power BI, Tableau, Looker, D3.js, Plotly, Recharts, or similar technologies • Experience building custom analytics applications rather than working exclusively in traditional BI tools • Knowledge of SQL, data modeling, ETL/ELT workflows, and cloud data platforms • Experience integrating Microsoft and Google Workspace services through APIs • Previous experience in consulting, client-facing delivery, or fast-moving project environments • Experience using LLMs or AI agents to support analytics, reporting, or data exploration • What Success Looks Like • You can quickly understand business needs, work effectively within an existing enterprise technology environment, and turn complex data into intuitive, actionable experiences. You are comfortable owning execution, collaborating remotely, and using modern AI-enabled workflows to deliver high-quality work efficiently.
Responsibilities
• Design and build enterprise-grade analytics and data visualization solutions • Develop interactive dashboards, reporting experiences, and data-driven applications • Use Python to transform, analyze, and prepare data for reporting and visualization • Build frontend features and visualization components using TypeScript • Integrate analytics solutions with enterprise Microsoft tools and Google Workspace • Translate business questions and stakeholder needs into practical technical solutions • Improve the usability, performance, and maintainability of existing analytics platforms • Use AI-assisted tools to accelerate development, analysis, documentation, and problem-solving • Collaborate with technical and non-technical stakeholders in a remote environment • Take ownership of deliverables from initial requirements through implementation
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