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Overview

The first technical hire at a VC fund is a unique role: part engineer, part product manager, part internal consultant. Getting it right means finding someone who can operate independently in an ambiguous environment while delivering real value. This chapter covers when to hire, what to look for, and how to grow the team over time.

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When to Hire Your First Data Person

The best funds hire proactively, before they realize they need it. But the right timing depends heavily on fund size and strategy. Signals that you’re ready:
  • Partners are spending significant time on manual data tasks that could be automated
  • You have a specific technical initiative that needs leadership (not just a vague “we should be more data-driven”)
  • The fund has conviction that data and technology will be core to differentiation, not just operational efficiency
  • You have budget for the hire and the tools, infrastructure, and data they’ll need
Signals that you’re not ready:
  • “Data-driven” is a buzzword in your marketing, not a real strategy
  • You expect one person to solve all technical problems while you figure out what you actually need
  • There’s no budget for data subscriptions, cloud infrastructure, or tooling beyond salary
  • Partners aren’t willing to invest time in defining problems and giving feedback
Many successful funds operate without dedicated technical staff. They use off-the-shelf tools like Affinity or Attio, outsource specific projects, and focus their energy on deal sourcing and portfolio support. The decision to hire should be driven by your fund’s specific needs and strategy, not by what other funds are doing. But if you’ve decided to hire, here’s what to look for.

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What to Look for in Your First Hire

Hiring advice from Ties Boukema at Dawn CapitalThe first tech hire should be able to sell the vision of what’s possible with data & tech internally and generate excitement among partners. They need to be self-motivated to identify opportunities where data, tech & AI can add value and pitch their own projects.Investors typically haven’t built data pipelines & complex solutions that scale, so their intuition on what’s possible (and how fast!) can be off. Great tech hires need to be proactive problem-identifiers who can independently scope and drive their own work rather than wait for assignments. This is different from typical banking or consulting analysts who are accustomed to working on urgent, well-scoped deliverables.You also need to be comfortable figuring things out largely on your own. If you come from a tech firm, there’s lots of engineers to ask for help. You will almost certainly be the most technical person at the firm, which is a different environment. On the flip side, you get a level of autonomy (and speed!) that is extremely hard to find unless you’re a founding engineer at a startup.
How much you weight vision-selling versus hands-on execution depends on fund size and team structure. At larger funds where you’re building a team, you can separate leadership (selling the vision, setting strategy) from execution (building the infrastructure). At smaller funds where one person does everything, vision-selling and technical depth have to coexist in the same hire. The ideal technical profile:
  • Broad software engineering skills
  • Data engineering depth (pipelines, databases, integrations)
  • VC domain knowledge
Technical skills are learnable. Product sense in a specialized domain like VC is harder to acquire. Hiring too junior is the biggest mistake funds make. Someone early in their career, even if technically capable, often struggles without external structure. Look for someone who has shipped projects end-to-end with minimal supervision.

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How Hiring Evolves

Don’t hire a second person until the first hire has identified clear workstreams to divide. The first hire’s job isn’t just building tools. It’s discovering what the fund actually needs: experimenting with different projects, identifying where sustained investment makes sense versus where quick wins are sufficient. When to hire a second person:
  • You have clear, separable workstreams (e.g., data infrastructure vs. internal tools vs. ML/AI initiatives)
  • The first hire is consistently underwater despite good prioritization
  • You’ve identified a big swing that requires dedicated focus and the first hire can’t abandon other responsibilities
When hiring specialists (ML engineers, data engineers, etc.):
  • Only hire specialists when there’s enough work to keep them occupied long-term
  • If you want to take a big swing on AI/ML, use the first hire to scope it and build the data infrastructure to support it, then hire someone dedicated to that initiative
  • For niche specialties with uncertain long-term demand, use consultants for the initial build. Larger funds often do this: bring in external help to ship something, then decide whether the workstream justifies a full-time hire
The pattern is: generalist first to figure out what matters, then specialists for sustained investment in specific areas.

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Compensation and Role Definition

VC fund technical roles sit awkwardly in the market. You’re not a big tech company with standard levels and compensation bands. You’re not a startup with equity upside. You’re a small team at a financial firm, which means different expectations on both sides. On compensation:
  • Base salaries are typically in line with tech companies
  • Carried interest (carry) is still rare for technical hires but becoming more common, especially at senior levels
  • Equity is non-existent (VC funds don’t have equity like startups do)
  • The value proposition is: interesting problems, high autonomy, direct impact, exposure to the startup ecosystem
On role definition:
  • Titles vary by firm. Some use “CTO,” others prefer “Head of Data” or “Head of Engineering”
  • Be clear about scope: are they building tools for deal flow, portfolio analytics, LP reporting, all of the above?
  • Define the reporting structure: do they report to a partner? The COO? Operating independently?
  • Beyond formal reporting, identify which stakeholders outside the investment team they’ll work with – operations, finance, investor relations – and ensure those people are bought in on the hire

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Red Flags in Candidates

Watch out for these warning signs: No evidence of independent ownership. Ask about projects they drove end-to-end. If every example involves working within a well-defined scope set by someone else, they’ll struggle in a VC environment. Dismissive of “boring” work. Data pipelines, integrations, and internal tools aren’t glamorous. If a candidate only wants to work on interesting technical problems, they won’t do the unglamorous work that actually matters. Can’t explain technical concepts simply. They’ll be working with non-technical people every day. If they can’t translate technical trade-offs into business terms, they’ll struggle to get buy-in for projects. (See Mistake #7).

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The Bottom Line

Your first hire needs broad software skills with depth in data engineering and VC domain knowledge. They should be able to sell the vision and deliver on it. Avoid hiring too junior. Let your first hire identify what actually matters before expanding the team. In Part 2, we’ll shift from understanding VC to the practical work of building: what tools exist, when to buy versus build, and how to put together a tech stack that serves your fund’s needs.