Data Engineering
Trusted data pipelines under every report and AI model.
We build data engineering foundations: pipelines from ERP, CRM, ecommerce and files into a warehouse, clean and documented models, quality checks, scheduled refreshes, and BI dashboards and AI features on top.
- Pipelines
- Warehouse
- Quality checks
Free 30-minute call with a senior specialist. Reply within one business day. Based in Jaipur, working with businesses across India.
- Discovery callFree 30 minutes with a senior specialist
- Scoped first phaseWritten scope, timeline and fixed price
- Build with weekly demosWorking software you can see every week
- Launch and supportGo-live with your team ready, then ongoing support
What is data engineering?
Data engineering designs the pipelines and storage that move data from business systems into a warehouse or lakehouse, cleans and models it and keeps it reliable for reports and AI. Stack18 builds data foundations for Indian companies, with quality checks, documentation and dashboards that people actually trust.
Last reviewed 30 October 2026 by the Stack18 delivery team in Jaipur. Security and AI governance · Pricing · About Stack18
| What it covers | Ingestion; Warehouse and models; Quality and reliability; Analytics and BI; AI readiness. |
|---|---|
| Typical first step | Discovery Sprint: 2 weeks, fixed price. |
| Build route | Fixed-scope first release: agreed in discovery; fixed price after discovery. |
| Long-term option | Transformation Pod: 12–36 months, annual contract. |
| Measured by | Report preparation time; Data freshness; Failed pipeline runs; Metric disputes. Each against an agreed baseline. |
| Governance | Human approval at 5 governance gates before anything reaches production. NDA available on request. |
| Delivery team | Based in Jaipur, India. A senior specialist replies within one business day. |
You need this when…
Tap the ones that apply to you.
Data Engineering, end to end.
Each capability is delivered by the same team on the Agentic SDLC, with your approval at every critical step.
Ingestion
Bring sources together.
- ERP, Tally, CRM and ecommerce sources
- API and file loads
- Change capture where needed
Warehouse and models
One clean source of truth.
- Warehouse or lakehouse design
- Business-friendly data models
- Definitions and documentation
Quality and reliability
Catch problems early.
- Automated data tests
- Freshness and anomaly alerts
- Lineage
Analytics and BI
Dashboards leaders trust.
- Dashboards and self-service
- Scheduled reports
- Metric definitions
AI readiness
Data ready for models.
- Feature and vector pipelines
- Access controls
- Privacy handling
A typical engagement, step by step.
Senior people lead every phase. Specialised AI agents on the Agentic SDLC handle the repetitive work, and nothing ships without your approval.
Discovery
Map how data foundations works today, the systems and data involved, and agree the first measurable outcome.
Design
Design the workflows, roles, integrations and approval points for data foundations.
Build in weekly demos
Your team sees working data foundations software every week, with changes agreed in the open.
Launch and support
Data migration, training, go-live support and a measured 30-day review against the baseline.
Start small. Scale when it works.
Every engagement begins with a short, fixed-price step so you can judge the fit before committing further. See pricing.
Discovery Sprint
Teams deciding scope and priorities before they commit.
- Duration
- 2 weeks
- Pricing
- Fixed price
- Scoped plan and timeline
- Team and fixed price
- Process map or prototype
Fixed-scope first release
Teams that know what they need built.
- Duration
- Agreed in discovery
- Pricing
- Fixed price after discovery
- Written scope and acceptance criteria
- Weekly demos
- Handover with documentation
Transformation Pod
Multi-system programmes that run for years.
- Duration
- 12–36 months
- Pricing
- Annual contract
- Dedicated cross-functional team
- Quarterly business reviews
- One accountable partner
What we have shipped, and what a typical engagement looks like.
Delivered projects link to the full case study. Illustrative scenarios describe typical engagements and are not named client results.
Retailer with five systems
- Challenge
- Reports disagree; analysts clean exports for days.
- What we do
- Warehouse with tested models and trusted dashboards.
Scoped in a 2-week sprint
Free data foundations review
Tell us where data foundations hurts most. We map what to fix first, whether ready-made or custom fits, and a realistic first step.
- Where to start and why
- Ready-made vs custom view
- Indicative timeline
We agree targets before we start.
Every engagement has a baseline and a target on the measures that matter to you, reported at every read-out.
Data Engineering questions.
Do we need a warehouse?
If you have several systems and recurring reporting pain, usually yes. For one system, simpler reporting may be enough, and we say so.
Which tools do you use?
We choose from established warehouse, orchestration and BI tools to fit your cloud, cost and team skills.
Can you work with Tally data?
Yes, through exports or interfaces your Tally set-up supports.
How long does a first version take?
A two-week Discovery Sprint scopes a first slice, often one source set and a handful of dashboards.
Who maintains the pipelines?
You do, with our documentation, or us under annual support.
Often combined with.
Data Analytics and BI
Dashboards.
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Read →Let's scope your Data Engineering project.
Tell us where you are and where you want to be. A senior specialist will reply within one business day with next steps.
- Free 30-minute consultation
- Indicative estimate after the call
- NDA available on request