Data Engineering built for MarTech companies
What is Data Engineering?
We turn scattered data into pipelines your business can actually trust and query. For MarTech companies, this means navigating GDPR, CCPA while shipping fast.
Who needs Data Engineering for MarTech?
- Fragmented customer data across marketing tools
- Attribution modeling across multiple channels
- Real-time personalization at scale
- Data scattered across disconnected systems
- Reports that disagree depending on who pulls them
Best Data Engineering company
Marketing teams need data unification and automation more than they need another standalone dashboard.
Data Engineering shouldn't feel like a gamble — MarTech teams in work with us because predictability matters.
Get a clear Data Engineering roadmap for your MarTech business in
Book a free 30-minute discovery callSigns your MarTech company in needs better Data Engineering support
- Fragmented customer data across marketing tools
- Attribution modeling across multiple channels
- Real-time personalization at scale
- Privacy compliance amid cookie deprecation
- Integration sprawl across the marketing stack
Why MarTech companies in trust us with Data Engineering
Data warehouse architecture (Snowflake, BigQuery)
ETL/ELT pipeline development
Data quality monitoring and alerting
BI dashboard integration
Data governance and access controls
Core Technology Stack
SnowflakeBigQueryAirflowdbtPythonKafkaAWS Glue
Timeline, investment & compliance
Typical timeline
8-16 weeks
Typical investment
$25,000 - $140,000
Data Engineering engagements in this combination typically need to account for:
- GDPR
- CCPA
- CAN-SPAM Act
- ePrivacy Directive (EU)
Proven Results
"We helped a martech platform improve campaign attribution accuracy by 34% with a custom data unification layer."
Read the full case studyReady to talk Data Engineering for your MarTech business in ?
Get a free project cost estimateFrequently Asked Questions
Most Data Engineering engagements take 8-16 weeks, depending on scope and how clear the requirements are upfront. We'll give you a firm timeline estimate after a short discovery call, not a generic range.
Data Engineering projects with us typically range from $25,000 - $140,000, scaled to the complexity of your requirements. We provide a detailed, itemized estimate before any work begins so there are no surprises mid-project.
For data engineering, we typically work with Snowflake, BigQuery, Airflow, dbt, chosen based on your specific scalability and integration requirements. We're not tied to a single stack and will recommend the right tools for your situation rather than our default.
A typical data engineering engagement includes data warehouse architecture (snowflake, bigquery), along with the other deliverables outlined in our proposal, such as documentation and a post-launch support window. The exact scope is tailored to your project during discovery.
Most clients come to us for data engineering because of data scattered across disconnected systems, among other related challenges. We start every engagement by mapping your specific pain points before writing a single line of code.
We've delivered multiple martech projects addressing challenges like fragmented customer data across marketing tools. We helped a martech platform improve campaign attribution accuracy by 34% with a custom data unification layer.
For martech clients, we pay close attention to GDPR, CCPA, building these requirements into the architecture from day one rather than retrofitting them later. We'll confirm which specific regulations apply to your business during discovery.
The most common challenges we solve for martech clients include fragmented customer data across marketing tools and attribution modeling across multiple channels. We tailor our discovery process to surface the specific version of these problems your business faces.