Data Engineering built for Ecommerce companies
What is Data Engineering?
We turn scattered data into pipelines your business can actually trust and query. For Ecommerce companies, this means navigating PCI DSS, GDPR while shipping fast.
Who needs Data Engineering for Ecommerce?
- Cart abandonment and checkout friction
- Inventory sync across multiple sales channels
- Page speed and Core Web Vitals under heavy traffic
- Data scattered across disconnected systems
- Reports that disagree depending on who pulls them
Best Data Engineering company
Every second of page load and every step of checkout friction directly costs conversion revenue.
If your Ecommerce business needs Data Engineering, we've already solved most of the hard parts.
Ready to talk Data Engineering for your Ecommerce business?
Book a free 30-minute discovery callThe real cost of getting Data Engineering wrong in Ecommerce
- Cart abandonment and checkout friction
- Inventory sync across multiple sales channels
- Page speed and Core Web Vitals under heavy traffic
- Personalization without compromising privacy compliance
- Peak-season scalability for flash sales
Our process for delivering Data Engineering to Ecommerce clients
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:
- PCI DSS
- GDPR
- CCPA
- Consumer protection regulations
Our Approach
Ecommerce software fails quietly if it isn't built for cart abandonment and checkout friction from the start. We scope for PCI DSS and GDPR requirements up front, and keep the review cadence tight enough to catch drift before it ships.
See our case studiesReady to talk Data Engineering for your Ecommerce business?
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 ecommerce projects addressing challenges like cart abandonment and checkout friction. We helped a DTC retailer increase checkout conversion by 19% through a custom headless commerce rebuild.
For ecommerce clients, we pay close attention to PCI DSS, GDPR, 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 ecommerce clients include cart abandonment and checkout friction and inventory sync across multiple sales channels. We tailor our discovery process to surface the specific version of these problems your business faces.