Why Business Operators Beat Data Engineers in 2026

For the last twenty years, the assumption inside every enterprise was simple: if you need data work, you need a data person. Hire engineers who know SQL. Hire scientists who know Python. Then, when the COO needs a number, queue it up behind everyone else’s tickets.

Alex Reichenbach, CEO of Structify — the AI data team for enterprises — thinks that whole org chart is now a liability. And his reasoning isn’t about AI replacing engineers. It’s about which direction the learning curve actually goes.

“I’m gonna say something that you might disagree with,” Alex says, mid-interview. “It’s easier to teach someone who knows the business processes how to data engineer than it is to teach a data engineer the business processes.”

That sentence is the contrarian thesis driving where Structify points its product. And it has uncomfortable implications for how enterprises should think about hiring, tool selection, and who actually does the data work in 2026.

The asymmetry between technical skill and domain knowledge

Domain knowledge takes years. The reason a CRO or COO is valuable in their role isn’t that they can write SQL — it’s that they know why this customer matters more than that one even when the contract size is identical, why this column is tracked but that column isn’t, why the deal flow looks the way it does. Those are nuances built up over years of actual operating, and they don’t transfer cleanly into a written spec.

Technical skill — the SQL, the Python, the understanding of join semantics — used to also take years. But that’s the asymmetry that’s collapsed. With tools like Structify, much of the technical skill is now ambient. The agent figures out how to query, how to join, how to clean. The user provides the strategy and the business intent.

“It happens because these businesses grow over time and there’s not as much central planning,” Alex explains. “It’s just what happens in any large organization. And since our agents are able to navigate this data, it means that these business people can make their pipelines reflecting the business logic without needing to explain it to us as much.”

The implication is uncomfortable: if domain knowledge is the part that doesn’t compress, and technical skill is the part that does, then the business operator is now the more valuable user of an AI data tool than the data engineer who’s been trained for the role.

Why the head of data hates this argument (and the COO loves it)

Alex’s sales experience confirms what the thesis predicts. When Structify went after heads of data as buyers, they hit resistance. When they pivoted to COOs and PMs — the people who are downstream of the data team — they found enthusiasm.

“At one point we tried to sell to heads of data and I realized that was a terrible sales process for us,” Alex says. The reason isn’t that data leaders are wrong about technology — it’s that the new tooling threatens the role they’re optimizing for, while the operators downstream are starving for it.

There’s a clean version of this asymmetry in one customer story Alex shared. A data lead at a large bank told him: “I have hundreds and hundreds of dashboards that I manage. I’m thousands of tickets behind. I have ownership of this company and I know I’m costing this company the bottom line. I would love if you can help us not be critically behind.” That data lead saw the asymmetry and welcomed it. The ones who don’t have equity in the outcome resist it.

What this means for hiring

The practical takeaway runs against most enterprise instincts. Hiring more data engineers to clear a backlog is the obvious move. The contrarian move is to invest in operators — give the COO, the head of rev ops, the finance lead access to a generation platform and let them write their own pipelines.

This isn’t theoretical. Alex describes the pattern repeatedly: “People act as data scientists who don’t have a data science career. They’ve never done it before. They know some of the language because they’ve operated in the spreadsheet finance world before, or they’ve operated in the academic world. But they’ve never directly done data science. And they’re just truly delighted with having the power to do it themselves.”

The economic argument is straightforward. A data engineer costs $200K+ in the Bay Area and ships through a ticket queue. An operator with a Structify-style tool costs nothing extra and answers their own questions in real time. The latency on insight collapses from weeks to minutes.

The new bottleneck

If technical skill compresses and domain knowledge doesn’t, the bottleneck shifts. The new bottleneck isn’t data team capacity — it’s whether your operators believe they’re allowed to do the work themselves.

“The biggest thing that is preventing more people from becoming users is just their self-belief that they can do more than what they have been trained to do in the past,” Alex says. The org has spent twenty years training the COO to file a ticket and wait. The unlearning is the hard part, not the technical onboarding.

For 2026 and beyond, the enterprises that adopt fastest will be the ones whose leaders give explicit permission to operators to do their own data work. The ones still pouring headcount into data teams are about to discover that the asymmetry has flipped.

FAQ

Why are operators waiting weeks for data team responses?

Because data engineering capacity hasn’t kept pace with internal demand. A typical enterprise data team is thousands of tickets behind, and operators have been trained to file a ticket and wait. Alex Reichenbach calls this a “BPO model” — even teams with internal data resources often run them as service organizations, allocating credits by hour. When credits run out, no data work happens that quarter.

Why doesn’t selling AI data tools to the head of data work?

Because the head of data sees the tool as a threat to the role they’ve been building, unless they have equity in the company’s outcome. Heads of data with broad ownership welcome capacity-extending tools. Heads of data optimizing for team size or career growth resist them. Alex Reichenbach found this by trying — Structify’s go-to-market shifted to COOs and PMs after heads of data proved a difficult buyer.

How does Structify let business operators do their own data engineering?

Structify is a generation platform: an operator types a natural-language request into Slack, and the platform writes a deterministic code pipeline that pulls, joins, and processes the data. The user supplies the business strategy (“find me companies that signed up in the last 24 hours and aren’t in HubSpot”), and the agent figures out the technical execution. The pipeline runs the same way every time once it works.

What outcomes do enterprises see when operators run their own pipelines?

Latency on insight collapses. Alex describes a portfolio tracking task that took him a month and a half to build manually in 2023, and now takes “less than a couple of minutes” on Structify. M&A teams that previously spent days manually classifying documents see the work go fully automatic. Finance teams stop chasing Excel formula errors across passed-around files because the entire pipeline is preserved and re-runnable.

What objection do operators raise when they first try data tools?

Two main objections, in this order. First: “I don’t have time to learn something new — I’ll just suffer through it this quarter.” Second: “It doesn’t work” — usually because they’ve tried tools that required clean, prepared data and theirs is messy. Alex says the LLM layer changes the calculus on both: less learning curve, more tolerance for unstructured inputs.

What’s the security path for getting started?

For financial services, healthcare, and other regulated sectors, Alex says the typical first step is a security questionnaire confirming SOC 2 compliance and HIPAA support. Structify completed both. After that, a deployment strategist runs a pilot to demonstrate the use case without requiring the customer’s team to learn the tool first.

Are data engineers being replaced?

No — they’re being redefined. Alex’s framing is that data scientists become “deployment strategists” who design the strategy and let agents execute it. Structify’s own FDE program re-employs data scientists as upskilled BPO operators who handle the most complex client requests. The strategy thinking remains valuable; the manual SQL writing doesn’t.

Why is domain knowledge harder to teach than technical skill in 2026?

Because technical skill — SQL syntax, join semantics, basic Python — has been encoded into LLMs and code generation tools. Domain knowledge — why this column matters, why this customer is different, what the business actually does — is built up over years of operating and resists compression. The asymmetry didn’t always exist. It’s a recent shift caused by AI tools that handle the technical layer.

What’s the right way to introduce a data tool to a non-technical team?

Lead with the interface they already use, not the workflow builder. Alex’s go-to demo is the Slack integration: tag the agent with a natural-language request, get a structured answer back in thread. Operators are already trained to interact with people in Slack. Replacing the data team teammate with an agent in the same channel is a low-friction switch. Showing a workflow builder first triggers “another tool to learn” objections.