← This weekAnalysisData engineering & the warehouse/lakehousedbtThe article explores the transition from traditional analytics engineering—focused on modeling data for dashboards—to the emerging role of 'context engineer,' which emphasizes modeling context for AI agents. The series begins with insights drawn from Gong's data, highlighting the importance of context in agentic AI applications.
MyDataWork POV — The transition from analytics engineer to context engineer appears to be an appealing advancement, yet it conceals a hidden risk: overconfidence in 'context' as a cure-all. Gong's own data might suggest paths to success, yet the term 'context' is dangerously nebulous. If we can't clearly define what 'context' entails in practical terms for agents, we risk building solutions on sand. The industry could end up mired in more abstraction, losing sight of tangible data insights that truly drive decisions.
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