We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at scale — across all devices and digital mediums, and our people exist everywhere in the world (15000+ experts across 26 countries, to be exact). Our work culture is dynamic and non-hierarchical. We are looking for great new colleagues. That is where you come in!

• 6+ years of experience in Data Analytics, Business Intelligence, Reporting, or BI Development.
• Strong hands-on experience with MicroStrategy (MSTR).
• Experience with MSTR Reports, Dashboards/Dossiers, Metrics, Attributes, Filters, Prompts, Intelligent Cubes, and Datasets.
• Experience with Logi Analytics / Logi Symphony, or strong experience in another BI platform with the ability to learn Logi quickly.
• Strong proficiency in SQL, including joins, aggregations, CTEs, window functions, and query optimization.
• Strong understanding of data analysis, data validation, and reconciliation.
• Experience developing reports, dashboards, and data visualizations.
• Ability to analyze existing BI solutions and understand underlying business and calculation logic.
• Good understanding of relational databases, data warehousing, and dimensional data modeling.
• Experience working with business stakeholders to gather and clarify requirements. Strong problem-solving, analytical, and communication skills

AI skills (required for all roles)

• Daily, fluent use of Claude Code and/or GitHub Copilot for implementation, refactoring, test generation, and code review
• Ability to establish team standards for AI-assisted development: effective prompting, trust-vs-verify discipline on generated code, security/IP guardrails, and reviewing AI-authored changes
• Working understanding of LLM fundamentals: context windows, tokens, model selection, and prompt/context engineering
• Experience integrating AI into developer workflows and agentic/automation tooling (MCP servers, AI-driven CI steps, codegen and doc-generation pipelines)
• Able to evaluate AI tooling pragmatically: measuring real productivity and quality impact, not hype