You're getting ready to sign, and now your data or engineering team needs a number. Not "a few weeks of effort here and there," an actual hours estimate they can hold against their own roadmap, and often one you need to bring to Finance before the ink is dry.

The short version: if you already have a data warehouse (Snowflake, BigQuery, Redshift, or Databricks) and reasonably clean marketing and outcome data, a realistic budget is 30 to 60 hours of data engineering time, with nearly all of it landing in the four to six weeks right after signature. This article walks through where those hours go, the two questions that come up most often while sizing the work, and the one phase worth building a buffer around.

The Phased Breakdown

Here's how the work typically breaks down for a customer sharing data through a warehouse connection:

Add it up and you're looking at roughly 30 to 60 hours, concentrated almost entirely in the four to six weeks after signature. This is consistent with what we see across warehouse-connected customers, and it's a good number to bring into a Finance conversation as-is, with one caveat below.

The One Phase Worth Building a Buffer Around

Connecting, building feeds, and backfilling are mechanical work. Your team knows how to do it, and it goes roughly as planned. Validation is different: it's where you find out whether the mechanical work matches the business logic underneath it.

The kinds of things that tend to surface during validation:

• A feed that looks daily but has a few weekly rollups mixed in

• Campaigns mapping to the wrong channel, or landing in an "other" bucket that should be split out

• Gaps in specific states or regions that only show up once the data is sliced geographically

• A metric definition that changed at some point in your backfill window, which affects how far back the history is actually usable

None of this means anything went wrong on your end. It's simply the phase where the data gets used the way it's actually going to be used, and that's when small mismatches become visible. What we'd recommend: give this phase either a little slack past its planned end date, or name one person on your team who can turn around a fix quickly if something comes up. Either one is enough to keep a two-week validation window from quietly becoming four.

Two Questions Worth Settling Before You Finalize the Estimate

Can we share curated views instead of raw tables?

Yes. Sharing purpose-built, read-only views rather than your raw underlying tables is a completely normal pattern, and for a lot of teams it's the better one: it keeps the scope of what's shared clean and easy to reason about. The requirements are straightforward: read-only access, daily grain, and a view that produces the agreed fields (spend, impressions, date, geography). Where the data physically lives underneath doesn't matter. Confirm the specific fields and grain with your Growth Advisor before your team builds the view, so it doesn't need to be rebuilt once schema review starts.

Why 18 to 24 months, instead of just 12 or the full 3 years?

Twelve months is workable, but it produces a less stable model, particularly around how it handles seasonality, since the model hasn't seen a full year-over-year comparison yet. Going beyond 24 months, out to a full 3 years, adds backfill work without a proportional improvement in what the model can tell you, and older data can actually introduce noise if your business or channel mix has shifted meaningfully since then. The one exception: if you run a strongly seasonal business where two full annual cycles genuinely change what's visible in the data, going further can be worth the extra effort. Outside of that case, 18 to 24 months, the range already reflected in the table above, is the target to plan around.

One More Thing to Check Before You Lock the Number

This estimate covers the data that lives in your warehouse. If any of your marketing channels don't, offline media, events, out-of-home, podcast, or anything managed entirely by an agency, those typically need a separate, smaller manual-sharing track that this estimate doesn't include. Before this number goes to Finance, it's worth a quick pass with your Growth Advisor to confirm every channel you plan to include is accounted for somewhere in the plan.

And if your team isn't at the point of having a queryable warehouse yet, the estimate above doesn't apply as-is: ad-platform direct access and manual sharing are both workable starting points, just with a different effort profile. Ask your Growth Advisor for the equivalent sizing for your setup.

How to Talk About This Internally

If you need to explain this scope to your own data or engineering leadership, here's language that tends to land well:

"This is a bounded, roughly 30-to-60-hour project, almost entirely in the first month after signature. The bulk of it is setting up read access and building two feeds we already know the shape of. The one phase that could run longer is validation, so we're building in extra time around that step, with one person named to turn around fixes quickly if something comes up."

Related Articles

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• How to fill out your Paramark Data Catalog

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