Most marketing data problems are data engineering problems
They show up looking like analytics problems. But when you dig in, the issue is usually a broken pipeline, a missed integration, or data that never made it from one system to the next. We build the infrastructure that makes everything else work.
The work that lives between departments
Marketing data infrastructure tends to fall into a gap. Marketing doesn't own it. IT considers it marketing's problem. The data team has priorities from finance and product. The result: a web of point-to-point integrations, screenshotted reports, CSV uploads, and Zapier workflows that mostly work, except when they don't.
We do the infrastructure work because someone has to, and because bad infrastructure sets a ceiling on every other marketing investment. You can't do serious attribution work on top of data that doesn't reconcile.
Data engineering
Data warehousing
BigQuery, Snowflake, and Redshift. Schema design, cost optimization, access patterns, and the dbt or SQL transformations that turn raw data into something usable.
Pipelines and ingestion
Custom ingestion from ad platform APIs, CRM exports, product event streams, and third-party tools. Built to run reliably without constant babysitting.
Cross-platform stitching
Joining ad spend, user behavior, CRM lifecycle, and revenue data into a single view. The foundation for honest attribution and blended ROAS reporting.
Identity and attribution models
Stitching user journeys across sessions, devices, and anonymous-to-known transitions. Attribution logic you can inspect, not a black box.
Systems integration
CRM to ad platforms
Offline conversion uploads, enhanced conversions, and conversion APIs for Meta, Google, and TikTok. So your CRM's closed-won data actually trains the bidding algorithms.
Email and lifecycle platforms
Klaviyo, HubSpot, Marketo, Customer.io. Event-driven triggers, segmentation, and the integration points that keep your lifecycle data clean.
Custom API and middleware work
Connecting anything to anything. Reverse ETL, webhooks, and middleware when the off-the-shelf integration doesn't do what you need.
CDP and event streaming
Segment, RudderStack, and mParticle as the connective tissue between your sources and destinations. Schema design and source/destination governance.
What a good data
infrastructure looks like
There's a simple test for whether your data infrastructure is healthy: can a new analyst answer a cross-platform question without asking three people where the data lives?
adMind built our marketing data warehouse from scratch. Two years in, we've added three new ad platforms and a new CRM without a single data pipeline emergency. That's not how this usually goes.
[Customer Name]
VP of Marketing Analytics
[Enterprise client]
How engagements typically start
Systems integration and data engineering work usually lives inside a Growth or Full Stack analytics package, because this kind of infrastructure only pays off when it's connected to the tracking, reporting, and analysis layers.
That said, we do take on standalone projects: a specific warehouse migration, a reverse ETL setup, a conversion API rollout, or a CRM-to-ad-platform integration. If you've got a defined problem with a defined scope, a fixed-price project is often the right shape.