Data Engineering built for Retail companies
What is Data Engineering?
We turn scattered data into pipelines your business can actually trust and query. For Retail companies, this means navigating PCI DSS, GDPR while shipping fast.
Who needs Data Engineering for Retail?
- Inventory visibility across online and physical stores
- Omnichannel customer experience consistency
- Point-of-sale and ecommerce system integration
- Data scattered across disconnected systems
- Reports that disagree depending on who pulls them
Best Data Engineering company
Omnichannel retail lives or dies on whether inventory and customer data actually sync in real time.
GarudLabs partners with Retail teams to deliver Data Engineering that ships on schedule.
Get a Data Engineering proposal tailored to your Retail goals
Book a free 30-minute discovery callWhy Retail companies struggle with Data Engineering
- Inventory visibility across online and physical stores
- Omnichannel customer experience consistency
- Point-of-sale and ecommerce system integration
- Personalization at scale across touchpoints
- Peak season infrastructure scalability
What makes our Data Engineering different for Retail businesses
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
Retail software fails quietly if it isn't built for inventory visibility across online and physical stores 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 studiesStart your Data Engineering project with a team that knows Retail
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 retail projects addressing challenges like inventory visibility across online and physical stores. We helped a retail chain unify online and in-store inventory, cutting stockout incidents by 33%.
For retail 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 retail clients include inventory visibility across online and physical stores and omnichannel customer experience consistency. We tailor our discovery process to surface the specific version of these problems your business faces.