Data engineering in asset management was always a context problem, not a technology problem. The analyst knows what the data means, the data scientist knows how to query it, and the data engineer sits two steps removed from the domain, building pipelines against a business problem nobody translated cleanly. Michael and Jhanvi make the case that AI collapses that chain: you can now apply context at the last mile, at inference time, instead of spending the first 99 miles encoding it into a warehouse that turns into cement. The security master edge cases, the identifier mapping across 300 names, the non-GAAP KPI alignment against Visible Alpha, all of it fits in a cheap model's context window.
So what does a data engineer do now? Build the context layer. Skills, agent instructions, tools, memory, and an observability layer that tells you which agents are failing and why before your users do, then harden pieces into deterministic code only once you actually understand them. Jhanvi and Michael also get into where this breaks: an agent harness that can't respect your data permissions, MCP connectors that authenticate as a service account instead of on behalf of the user, and the question of what accountability looks like when the work is done under an agent's name instead of yours.
About Hedgineer
Hedgineer is building the AI platform for institutional investing — deploying agents, skills, and data connectors directly inside hedge funds and asset managers to transform investment and operational workflows.
The Hedgineer Podcast follows CEO Michael Watson and COO Jhanvi Virani as they navigate the frontier of AI adoption in finance, sharing unfiltered perspectives from the teams, guests, and problems they work with every day.
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