Unify the data
Marketing data engineering
One warehouse, one set of numbers, every channel reconciled to finance, so reporting stops being a debate.
What we do
We design and build the data layer under your marketing: pipelines from Shopify, Amazon, Meta, Google, TikTok, Klaviyo, Gorgias and your finance system into a warehouse (BigQuery, Snowflake or Redshift), a documented data model, and dashboards that replace the Monday-morning spreadsheet.
What you get
- Source audit and data-quality baseline
- Warehouse, pipelines and a versioned data model
- Reporting layer (Looker, Looker Studio, Metabase or Power BI)
- Documentation and training so your team can extend it
Research and answers on this topic
Data engineering: a guide for ecommerce brands
Data engineering for ecommerce brands: what Shopify, Meta, Google Ads and Klaviyo data needs, the four-layer stack, what it costs and where to start.
AI consultant cost in 2026: what consultancies charge
AI consultant cost in 2026 from published prices: about $1,000 for a first dashboard to $1M+ to train a custom LLM. 64 of 69 consultancies publish none.
Agentic checkout for a Shopify brand: selling in ChatGPT, Google AI Mode and Copilot
Shopify's agentic storefronts are on by default. Where each AI channel takes payment, what it does to tracking and customer data, and what to change first.
Fivetran vs Airbyte vs custom pipelines for a Shopify brand's marketing data
Fivetran vs Airbyte for a Shopify brand: how each charges, published prices, where BigQuery's free transfers fit, and when a custom pipeline is worth it.
How do I build a marketing data warehouse from Shopify, Klaviyo, Meta Ads and Google Ads?
How to build a marketing data warehouse: connect Shopify, Klaviyo, Meta Ads and Google Ads, what each route costs, and how to make the numbers join.
Related work
- Read the story
Sales operations rebuild for a South African retailer
- +39%
- conversion
- −23%
- sales cycle
- +22%
- revenue in Q1
Common questions
- Which warehouse do you recommend for a DTC brand?
- For most $5M–$200M brands, BigQuery or Snowflake. Both are low-maintenance, cost-effective at this volume and work with every connector we use. We pick based on your existing cloud and your team's skills.
- Can you reconcile marketing data to our finance numbers?
- Yes. Reconciliation to the ledger is a standard deliverable. The data model carries refunds, discounts, shipping and COGS so contribution margin matches what finance reports.
- Will we own the warehouse and the code?
- Entirely. The warehouse sits in your cloud account, the code lives in your repository, and the documentation is written so your team or any other partner can maintain it.
- How is this different from buying a dashboard tool?
- Dashboard tools show you data; they do not clean, join or reconcile it. We build the layer underneath so whichever dashboard you use shows numbers that agree with each other.
Talk to us about your stack
A 30-minute discovery call with our founder. We will tell you honestly whether an engagement makes sense and where the first return would come from.