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Pipelines that hold
Ingest, transform and quality checks for the sources that matter to the next decision, not every table in the company.
04 / Services
Data Engineering & Analytics
Pipelines, warehouses and dashboards scoped to the decisions you need to make, not a multi-year data programme.
Rows processed
0–50%
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40%
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Jan
+20%
Feb
+31%
Mar
+42%
Apr
+51%
In short
Do we need a data warehouse before we can use AI?
The Cubeless Company does data engineering as a path from source systems to one decision, not a multi-year warehouse programme. We ship ingestion, a trusted model and a dashboard or export for a single question first — typically in four to eight weeks — then extend to sources that earn their place. Conflicting reports are treated as a definitions problem: each metric is named once and every dashboard reads that model.
| Alternative | Choose it when |
|---|---|
| A full warehouse programme | You have years, a platform team, and every source equally important. |
| A thin slice | One decision or AI feature needs reliable data now. |
| Another dashboard on raw tables | The number already exists; the team just cannot find it. |
- First pipeline
- Typically 4–8 weeks in production
- Scope
- One decision, then the sources that earn a place
- AI-ready
- Usable data where the model will look — not everywhere
- Related work
- RPMCorofy LLC
Outcomes
What this engagement moves.
Clear results we design toward from the first scoped release.
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Reliable feeds into the tools people already open
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Metrics that match how the business actually talks
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A foundation ready for AI where it earns its keep
What’s included
Built as a system, not a pile of tickets.
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Warehouses and models
Schemas and metrics your analysts and product teams can trust week after week.
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Decision surfaces
Dashboards and exports that answer specific questions, not vanity charts.
How we deliver
From scoped first release to production.
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Name the decision
What must be true, for whom, how often, then the minimum data path that serves it.
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Ship a thin slice
One pipeline, one model, one surface in production before we widen the map.
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Harden and extend
Monitoring, ownership and the next sources that earn their place.
More services
Keep exploring.
Data Engineering & Analytics / FAQs
Questions we get asked.
What teams usually want to know before starting data engineering & analytics work with us.
A working path from your source systems to a decision. That means ingestion and transformation for the sources that matter, a warehouse and data model your analysts can trust, quality checks that fail loudly, and a dashboard or export that answers a specific question. We ship one pipeline end to end before widening the map.
Not usually. You need reliable, well-modelled data for the specific decisions or AI features you are building next. We scope the minimum data path that serves those, get it into production, then extend to further sources as they earn their place — which is far faster than a multi-year data programme.
Conflicting reports are almost always a definitions problem, not a tooling problem. We agree what each metric means with the people who use it, implement those definitions once in a shared data model, and point every dashboard at that model, so the number is calculated in one place instead of five.
We work with PostgreSQL, BigQuery, Snowflake and Redshift as warehouses, Python and SQL for transformation, orchestration with Airflow or Celery, and dashboards in the tool your team already opens. We choose based on your existing stack, data volume and team skills rather than a fixed preference.
A first pipeline, model and dashboard in production typically takes four to eight weeks. We deliberately start with one decision rather than a full data platform, so the value arrives while the wider programme is still being planned.