Senior Data Engineer, Ads & Attribution

You will build a company's entire marketing data layer from scratch. Not maintain someone else's, not tweak dashboards. Build it, own it, and decide what it runs on.Right now there are two disconnected warehouses and no way to answer a simple question like what ROAS looked like this week across the company. That is what you are here to fix. US company, fully remote team across Europe, B2B contract.Rate: $5,000 to $9,000 per month, up to $11,000 for someone exceptional. Including VAT. Some companies call this role Analytics Engineer or Growth Data Engineer. Here it is 90 percent engineering. What you'll doBuild ingestion pipelines in Python against the Google Ads, Meta, Apple Search Ads, AppsFlyer and RevenueCat APIs, with the rate limits, schema changes and backfills that come with themDesign the data model and warehouse from scratch. Postgres today, but you pick what actually holds up at this volumeWrite the identity resolution layer yourself. Deterministic and probabilistic matching across web and mobile, click IDs, device identifiers, deduplicated event streamsSolve performance at click-level scale. Partitioning, indexing, query planning, storage cost. This will be the heaviest load in the entire stackMerge two existing warehouses into one model, including migrating the legacy Python pipelines or replacing them entirelyBuild the reconciliation logic that catches when platform numbers and your own numbers disagree, then find out why What this role is notNot a BI or dashboarding role. Not an analytics role where SQL and a modelling tool cover the job. Not a marketing role with a technical flavour. And not a lead position, at least not yet. You are the first person doing this here. What you bring5+ years as a data engineer, with real ownership of a marketing or attribution data layer you built yourselfStrong Python, production pipeline code, not scriptsDeep understanding of database performance at high volumeHands-on experience with at least one ad network, Google Ads or Meta preferred, including the platform itselfProven work on attribution and user deduplication across web and appBackground in consumer mobile apps, gaming or e-commerce, and you worked on the ad and attribution side yourself, not next to itAble to explain technical decisions to a non-technical VP of Growth, and turn vague requests into something that works. This is where most candidates so far have fallen downYou don't have to tick every box. If you have owned this kind of system end to end, the rest follows. Knock-out criteria4 years of experience or lessRepeated short stints across companiesNo exposure to any MMP or attribution toolingClaiming 100 percent attribution accuracy, which is not possibleNot available around the daily stand-up at 15:00 UK time PracticalRemote anywhere in Europe. Daily stand-up at 15:00 UK time, available an hour either side. Beyond that you set your own hours.B2B contract, monthly fee, output-based, no time tracking. Three month ramp-up period. Roughly 14 days off per year, not tracked. Why this oneFull stack freedom. DigitalOcean and Postgres today, but nothing is sacred. BigQuery, ClickHouse, Snowflake, whatever you can defend. You can rip out the existing Python if you want to.First hire in this area, so you set the architecture instead of inheriting one.Interviews start next week. I run this search directly with the hiring team, so you get real feedback fast instead of silence. Tags: Data Engineering, Python, Attribution, Marketing Analytics, AppsFlyer, Google Ads, PostgreSQL, BigQuery, ETL, Growth