Service
Data engineering and analytics services
If your team spends days each month stitching reports together from separate systems, the problem is not analysis, it is plumbing. Stackvala builds the data pipelines, storage and dashboards that make reliable numbers available when people need them.
From scattered data to dependable reports
Data engineering is the work of collecting data from your applications, cleaning it, storing it in a structure suited to analysis and delivering it to the people and tools that use it. Analytics sits on top: dashboards, metrics and models that turn data into decisions. Without good engineering underneath, dashboards disagree with each other and nobody trusts them.
- Data pipelines (ETL and ELT) from applications, databases and APIs
- Cloud data warehouses and data lakes
- Data quality checks and reconciliation
- Business intelligence dashboards and self-service reporting
- Product and customer analytics
- Data foundations for AI and machine learning
Common data problems we solve
| Problem | Why it happens | Our approach |
|---|---|---|
| Different teams report different revenue figures | Metrics defined separately in each tool | One agreed metric layer in the warehouse |
| Reports take days to prepare | Manual exports and spreadsheets | Automated pipelines and scheduled refresh |
| Dashboards are slow | Queries run against production databases | Separate analytical store with modelled tables |
| Nobody trusts the data | No tests or lineage | Automated quality checks and documented sources |
| AI projects stall | Data is incomplete or inaccessible | Clean, governed datasets prepared first |
What we build
Data pipelines
Reliable, monitored flows from source systems to your warehouse.
Read about data pipelinesWarehouse and lakehouse
Cloud storage modelled for fast, consistent analysis.
Read about warehouse and lakehouseDashboards and reporting
Role-based views for finance, operations, sales and product.
Read about dashboards and reportingData for AI
Prepared, governed datasets for search, forecasting and assistants.
Read about data for AIKey points
Our approach
Start with decisions
We list the decisions the business needs to make, then work backwards to the data required.
Model once, use everywhere
Shared definitions for revenue, active users and churn avoid arguments.
Test the data
Freshness, completeness and duplicate checks run automatically.
Respect privacy
Access controls, masking and retention aligned to the sensitivity of the data.
Right-size the stack
A small business rarely needs a large streaming platform; we choose the simplest tools that work.
Typical architecture
| Layer | Examples | Purpose |
|---|---|---|
| Sources | Databases, SaaS apps, files, event streams | Where data originates |
| Ingestion | Managed connectors, custom API pulls | Move data reliably |
| Storage | Cloud warehouse or lakehouse | Single analytical store |
| Transformation | SQL models, tests, scheduling | Clean and shape data |
| Serving | BI tools, embedded dashboards, APIs | Deliver insight to users |
These systems run on infrastructure managed through our cloud and DevOps services.
Process
Discover
Key decisions, data sources, owners and current pain.
Design
Data model, metrics definitions and architecture.
Build
Pipelines, models and first dashboards in short cycles.
Validate
Reconcile against existing reports with the business.
Operate
Monitoring, documentation and handover.
Read how we work for communication and handover, and pricing models for engagement options.
Working with Stackvala
A simple, low-risk way to start
Free discovery call
Share your goal and get a clear recommendation. No obligation.
NDA first
We sign before you share any project details.
Milestone billing
Payments tied to delivered milestones, with written scope.
You own it
Code, designs and documentation are assigned to you.
FAQ
Questions people ask us
Cannot find your answer? Tell us about your project and a real person will reply within one business day.
Contact StackvalaWhat is data engineering?
It is building the systems that collect, clean, store and deliver data so it can be analysed reliably.
Do we need a data warehouse?
If reports draw on several systems, or dashboards slow your applications, a warehouse usually helps. For very small data sets a simpler setup may be enough.
Which BI tools do you work with?
We work with common tools such as Looker Studio, Power BI, Tableau and Metabase, or build embedded dashboards in your application.
Can you fix inconsistent metrics?
Yes. We define metrics once in a shared model and test them so every dashboard agrees.
How do you handle sensitive data?
With access controls, masking, encryption and retention rules, designed with your compliance advisers.
How long does it take to build a data platform?
A focused first version with a few sources and dashboards often takes a few months, then grows in phases.
Can this support AI features?
Yes. Good data foundations are the first step for search, forecasting and assistants.
Start a project
Have a project in mind?
Tell us what you are building. A real person reads every request and replies within one business day.
- NDA before you share details
- Written scope and price options
- You own the code