Data Engineering
One source of truth instead of ten spreadsheets
Pipelines that collect data from every system, clean it, and store it in a warehouse your dashboards, AI tools and teams can trust.
- 4–12 weeks
- Project
Sound familiar?
“Sales data is in the DMS, finance in Tally, online orders in Shopify, and everything else in Excel. Nobody agrees on the numbers.”
What we deliver
- Source audit and data model
- Automated pipelines (ERP, POS, DMS, Shopify, Excel, APIs)
- Data warehouse setup (cloud or on-premise)
- Data quality checks and alerts
- Master data (products, outlets, customers) clean-up
- Documentation and access control
What changes
Consistent numbers across departments
Reports and AI built on clean data
Less time cleaning, more time deciding
Outcomes are described qualitatively until we can share measured results from client projects.
Who it's for
- Companies with 3+ business systems
- Enterprises planning AI
- Analytics teams
Not on the list? Tell us how you work today — we'll say honestly whether this fits.
Timeline
4–12 weeks
FAQ
Data Engineering: common questions
Which warehouse?
BigQuery, Snowflake, PostgreSQL, others — chosen on cost and scale.
Real-time?
Hourly or near real-time where needed.
Who maintains pipelines?
We do, under AMC, or hand over.