What could your smartest data engineer be doing instead of a 6-month data migration?

As data engineers, we talk a lot about high-leverage work that, once done, continues to pay off. High-leverage engineers are good at what they do because they don’t just solve problems, but build systems, frameworks, and automations that multiply the efforts of everyone around them. They make the entire organization move faster.

Data migrations are the opposite. 

They’re short-term, fragile, and often done under pressure. They rely on tribal knowledge, introduce risk with every line of rewritten SQL, and burn months of calendar time, often without leaving anything reusable behind.

And yet, because the stakes are so high, they get prioritized, resourced, and handed to the most trusted people on the team. What starts as a “quick port” of logic across data warehouses turns into a months-long grind that pulls your best engineers into what might be the lowest-leverage activity in analytics work. 

The way we handle migrations today doesn’t scale. It drains momentum, delays roadmaps, and turns high-performing data engineers into full-time translators and spreadsheet auditors. 

The most expensive manual labor on the planet

Every data engineering leader knows the moment when you’ve committed to a new warehouse, and someone has to rewrite, port, and validate hundreds (maybe thousands) of pieces of logic to make sure everything still works.

Who do you trust with that kind of responsibility? Your best people. Not because they asked for it. But because you can’t afford to have this go wrong. They’re the ones who can spot edge cases buried in stored procedures, understand where logic deviates from intent, and handle brittle legacy code with care.

So, the migration begins with a kickoff and a vague spreadsheet of models to rewrite, maybe a Slack thread full of context that no one has time to read, and an internal doc labeled “Migration Plan – WIP.”

Just like that, your most valuable IC is pulled off roadmap work and buried in SQL rewrites, validation tasks, and manual cross-checking for months.

By the time the migration is done, you’ve traded six months of your top data engineer’s time (or worse, an entire task force is slurped into the massive endeavor) for a one-time success. There’s no compounding value, no reusable system, just duplicated logic in a new home, and a lot of relief that it’s over–if it ever ends. 

It’s manual labor at scale, and it’s incredibly expensive, both in terms of direct cost and opportunity cost.

Let’s do some math and put a price tag on it

Let’s say you’re migrating 100 models. Do you have an idea of how much it would cost to migrate them all?

$348,923. That’s how much a single migration project can cost your org in engineering time alone. We’ll show you how the math works out with our free Data Migration ROI calculator.  

These 100 models you’re migrating could be tables, views, stored procedures, or dbt models. (That’s a conservative estimate for most production data stacks.)

You assign a single full-time data engineer to the job. They make $140,000/year, and industry-standard labor overhead coefficients put it closer to 2.7x which becomes $378,000 when you account for true labor overhead (benefits, tools, meetings, and so on).

Now, at one day per model (a generous assumption), that’s 100 days or 1,920 hours spent on tedious translation and row-by-row validation–and that’s if nothing else goes wrong. 

Datafold’s Migration Agent (DMA) can handle 80-90% of the validation and QA burden by translating code automatically, validating results at the value level, and surfacing any mismatches across systems. In the exact same scenario, DMA can cut your project timeline from 60 weeks to 12. This saves you nearly $350,000 in cost, and gives your team back almost a year of productive engineering time.

It frees your team to focus on what actually moves the business forward. 

What could your team do with the other 48 weeks?

What didn’t get built while your best people were firefighting

Data engineers are expensive, but misallocating them is even costlier. When your smartest people are trapped in migration mode, they’re not:

• Improving query performance across the org
• Building tooling to empower analysts
• Rolling out CI/CD for the data team
• Tackling the root causes of downtime
• Mentoring the next generation of technical leaders

Migrations are high-stakes, but that doesn’t make them high-leverage. In fact, the more critical the migration, the more important it is to keep your best engineers in a strategic role: reviewing outputs, guiding structure, and making the hard calls. Not buried in brittle translation work.

When the migration “finishes,” leadership sees a box checked. They might also notice everything that didn’t get built during that stretch. The roadmap delays, the missed improvements, and the quietly burnt out data engineer, wondering why they feel like a very expensive (glorified, even) human diffing tool.

So why are we still partying (I mean, doing migrations) like it’s 1999?

We’re asking engineers to do tedious, manual work that doesn’t align with how we build anything else in the modern stack.

We define transformations in version-controlled code. We test them with assertions and CI checks before merging. We track data lineage automatically and orchestrate production pipelines in Airflow or Dagster. LLMs help autocomplete documentation and generate SQL macros, and we version control everything in Git.

And yet… we’re still validating data migrations by manually copy-pasting SQL into spreadsheets and praying the row counts match.

Why?

• Because there’s no established way to automate the hard parts
• Because migrations always feel like a one-off
• Because we assume that someone just has to suffer through it
• Because we like pain

Automate the boring parts and validate what matters

There’s a better way to approach data migrations: 

1. Automate the tedious parts
2. Validate the risky ones
3. Let your data engineers review, not rewrite

That’s the idea behind the Datafold Migration Agent (DMA), built to automate the two most painful phases of a migration: SQL translation and data validation. DMA uses a combination of AI-powered code conversion, value-level data diffing, and automated feedback loops to deliver accurate, testable, and production-ready migrations without the weeks (or months) of manual effort.

Instead of spending hours rewriting stored procedures by hand, DMA translates them automatically into your new platform’s SQL dialect or transformation framework (e.g., dbt, Coalesce.io, Dagster, Azure Data Factory, you name it). Then, using frozen input data and precise data diffs, it compares the outputs of legacy vs new code executed in legacy and new databases—row by row, value by value—to ensure nothing’s been lost in translation. 

If anything is off, DMA re-generates the code, fine-tunes the logic, and repeats until parity is achieved.

The result? Engineers spend a fraction of the time reviewing and refining, not rewriting from scratch. Stakeholders get proof of accuracy they can trust. And teams move 6x faster, not by cutting corners, but by cutting out the parts that never needed to be done by hand in the first place.

Let engineers engineer

You don’t hire principal engineers to become human diffing tools.

You don’t pull your best minds off the roadmap just to wrestle with brittle SQL rewrites when a machine can handle that.

Data migrations don’t need to be slow, risky, or soul-sucking (did we mention the risk of burning out engineers on these brutal, thankless projects?). They just need to be treated like software: tested, automated, repeatable.

So ask yourself: Do you really want to sink your smartest engineer into three months of brittle SQL rewrites? Or do you want them building the systems that power your next phase of growth?

We’ve helped teams cut migration timelines by up to 6x: not by replacing engineers, but by giving them superpowers. (If you missed our retro superhero-themed microsite featuring interviews with 7 data practitioners, check it out here. It’s as fun as it is insightful.) And if you’d like to see how DMA can accelerate your migration while ensuring complete accuracy, book time with our team.