Restatements handled
Attribution windows re-pulled instead of freezing stale numbers.
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Ad data engineering
The ad data engineering team behind performance marketing.
We design and run the data layer performance marketing lives on: pipelines from every ad platform, a modeled warehouse your team can trust, and reporting that updates itself. Built by engineers who've shipped this exact stack for agencies managing thousands of ad accounts.
- Ad platform pipelines
- dbt + Snowflake
- Agency scale
What you get
Pipelines that expect ad data's quirks.
Metrics that mean one thing
dbt models on Snowflake, BigQuery or Redshift, tested.
Broken feeds caught first
Monitoring flags a failure before it reaches a client dashboard.
Your team owns it
Documentation and handover, not lock-in.
Services
Every layer of the data stack.
ETL & ELT pipelines
Incremental, orchestrated, tested pipelines that re-run without a babysitter.
Learn moreAd platform API integrations
Amazon Ads, SP-API, Google, Meta, TikTok and retail media, normalized into one schema.
Learn moreReliability & monitoring
Freshness, volume and schema drift alerts, plus reconciliation to platform figures.
Learn moreMarketing data warehouse
dbt on Snowflake, BigQuery or Redshift: every ad platform modeled, tested and documented.
Learn moreConversions API
Server-side tracking for Meta CAPI, Google Enhanced Conversions and TikTok Events API.
Learn moreAmazon Marketing Cloud
AMC setup, audience uploads and queries, with clean room results in your own warehouse.
Learn moreThe problem
Why generalist data engineering fails for ad data.
Ad data is its own discipline. Attribution windows restate yesterday's numbers, every platform reports conversions differently, rate limits throttle the exact reports you need most, and a schema change in one API can silently corrupt a quarter of client dashboards. Generalist data teams treat these as edge cases; for an agency they are the daily workload. We build pipelines that expect them.
What an engagement covers
What's included.
- Ingestion pipelines from Amazon Ads, SP-API, Meta, Google, TikTok and retail media networks
- A modeled warehouse (dbt on Snowflake, BigQuery or Redshift) where metrics mean the same thing everywhere
- Restatement-aware syncs that re-pull attribution windows instead of freezing stale numbers
- Client- and account-level reporting marts sized for multi-tenant agency use
- Monitoring that flags a broken feed before it reaches a client dashboard
- Documentation and handover, so your team owns the stack, not us
How we work
How we run it.
Step 1
Audit the current data flow account by account: where numbers come from and where they break
Step 2
Stand up ingestion for the highest-spend platforms first, so value lands in weeks
Step 3
Model metrics in dbt with tests, so ROAS in one dashboard equals ROAS in another
Step 4
Layer on automation and alerting once the foundation is trusted
What we use
Tools, platforms and methods.
- Amazon Ads API
- SP-API
- Meta / Google / TikTok APIs
- dbt
- Snowflake
- BigQuery
- Airflow
FAQ
Common questions.
What does an ad data engineering team actually do?
It builds and operates the data infrastructure behind advertising: API integrations with ad platforms, ETL and ELT pipelines, a modeled data warehouse and automated reporting. Unlike a media agency, it does not manage campaigns. It builds the systems the campaign team relies on.
How is ad data engineering different from general data engineering?
Ad data has quirks general pipelines mishandle: attribution windows that restate historical data, per-platform conversion definitions, aggressive API rate limits and multi-account structures. Pipelines built for ad data re-pull restatement windows, normalize metric definitions across platforms, and are designed around those rate limits from day one.
Do you work with agencies or in-house marketing teams?
Both. Primarily performance marketing, Amazon and retail media, and ad-tech agencies, including white-label arrangements where we operate as your engineering team. We also work with in-house teams running meaningful ad spend across multiple platforms.
How long does it take to stand up an agency data stack?
A first working slice, one or two platforms flowing into a modeled warehouse with live dashboards, typically ships in 4 to 6 weeks. Full multi-platform coverage with automation and alerting is usually an 8 to 14 week build, shipped incrementally so reporting improves every sprint.
Go deeper
Guides from our engineers.
Guide
How to Build a Marketing Data Warehouse with dbt and Snowflake
A practical guide to building a marketing data warehouse with dbt and Snowflake: architecture, modeling layers, metric definitions, and the mistakes that sink these projects.
Read the guideGuide
ETL Pipelines for Amazon Sellers: How to Unify SP-API, Ads API, and Inventory Data
Amazon sellers juggle data across SP-API, the Ads API, and spreadsheets. Here's how a proper ETL pipeline unifies it into one reliable source of truth.
Read the guideGuide
How to Scale an Amazon Agency: Data Infrastructure Lessons from 35 to 3,500 Clients
Scaling an Amazon agency from dozens to thousands of clients breaks manual reporting completely. Here's the infrastructure that makes that scale possible.
Read the guideRelated
Related services.
Talk to an engineer.
Book a free 30-minute call with a senior engineer. Tell us which platforms you run and where the numbers break.