Ideal For
Who this engagement fits best
- Teams whose dashboards no one trusts
- Companies preparing data for AI / RAG use cases
- Series B+ orgs formalizing their data platform
- Analytics leads stuck on brittle Airflow pipelines
Not quite the right fit? Browse other services or reach out and we'll figure it out together.
Outcomes
Results clients see
One trusted warehouse layer across product, finance, and CS
From batch nightly to near-real-time for most metrics
dbt tests on every model before it ships to BI
Self-serve analytics replacing engineering tickets
See similar results in the case study archive.
Process
How we work together
A structured approach to data engineering that delivers results.
Discovery
Map data sources, consumers, and use cases
Architecture
Design data infrastructure and governance
Build
Implement pipelines and data models
Enable
Train team and establish practices
Curious what each phase looks like in detail? Read the full process page.
Deliverables
What you get
Tangible outcomes from every engagement, not just slides.
Every deliverable is owned by you on day one—your repo, your cloud, your accounts. Want to see real artifacts from past engagements? Visit the work archive.
Pricing
From $20,000
Get the warehouse and modeling layer right, then enable your team to own it.
Engagement model: Project + analytics retainer
- Source mapping
- Quality assessment
- Architecture proposal
- Tool recommendations
- Warehouse setup
- ELT pipelines
- dbt models
- BI layer + training
- Ongoing dbt model dev
- Pipeline operations
- Stakeholder support
- Quarterly upgrades
Need a custom scope? See full pricing details or request a custom quote.
Client Voices
What teams say
Anonymized quotes from recent engagements.
“Our finance team and our growth team finally agree on the numbers. That alone paid for the project.”
“Replaced two full-time data engineering hires we had been struggling to make. Faster output, less drama.”
FAQs
Frequently asked questions
Answers to the most common questions about data engineering engagements.
Snowflake or BigQuery?
Either-choice is driven by your existing cloud, team, and price/perf needs. I have production experience with both.
Do you do reverse ETL?
Yes-Hightouch or Census typically. Most operational analytics use cases need it.
Can the data layer feed our AI features?
That's a key reason most clients hire me. Data engineering and AI engineering work hand-in-glove.
Have a question that isn't here? Ask directly—I reply personally to every message.
Industries
Where this work lands
Sectors where this service has shipped real outcomes.
Reading
Related insights
Posts on topics adjacent to this engagement.
Or browse all insights.
Related
You might also need
Services that work well together with this engagement.
Ready to get started?
Let's discuss how data engineering can help your business. Most projects kick off within two weeks of the first call.