Warehouse and lakehouse
Design, migration and cost control on the platform that fits your team and cloud: AWS, Azure or Google Cloud.
SnowflakeDatabricksBigQueryPostgres
Services / 01 Data engineering
We consolidate scattered systems into a governed warehouse or lakehouse, with pipelines that are modelled, tested, observable and documented. Everything else you want to do with data depends on this layer.
- Typical first result
- 6 weeks
- Works with
- AWS, Azure, Google Cloud
- You own
- All code and infrastructure
customers→ dim_customerIllustrative
| Customer | Region | Revenue | Updated | |
|---|---|---|---|---|
ACME Ltd. | north | 4,200 | 03/07/26 | |
Acme Limited | N. | 4200.00 | 2026-07-03 | |
acme ltd | null | 4.2k | Jul 3 | |
Borealis Inc | South | null | 2026-07-02 | |
Cobalt & Co | WEST | 1,980 | 2/7/2026 | |
delta retail | east | 12k | 2026-07-01 |
6 rows from 3 systems, in 3 formats.CRMERPSheet
Sound familiar?
What we usually walk into.
Reports wait on a weekly export.
Decisions are made on last month’s picture, and one analyst becomes a bottleneck.
Scheduled, monitored pipelines from every source
Three definitions of “customer”.
Each team counts differently, so every meeting starts with an argument about the numbers.
Modelled, deduplicated core tables with owners
Pipelines break silently.
Someone notices a wrong number days later, and trust in data drops a little more.
Tests at every step and alerts to the right owner
Only one person understands the ETL.
Every change is risky, and holidays are a business continuity problem.
Version-controlled, documented code your team can run
What we build
The foundation, engineered.
We start from what you have and change only what earns its place.
Ingestion and integration
Managed connectors where they fit, custom extractors and change data capture where they do not.
FivetranAirbyteKafkaCDC
Modelling
Clean, documented models in dbt, with clear layers from raw to business-ready.
dbtSQLPython
Orchestration
Scheduled and event-driven runs with retries, backfills and clear dependencies.
AirflowDagsterPrefect
Quality and observability
Tests, freshness checks, lineage and alerting, so problems are found before users find them.
dbt testsGreat ExpectationsOpenLineage
Governance and metadata
Ownership, metadata management, documentation, role-based access and audit, without slowing people down.
CatalogsRBACMasking
Pipeline run
Bad data stops at the gate.
Every pipeline we build is tested at each step. When a source sends something broken, the run stops before it reaches a dashboard or an AI answer, and the right person is told.
- Tests on keys, nulls, ranges and freshness
- Lineage from every dashboard back to its source
- Alerts routed to the owner, not a shared inbox
revenue_dailyorchestrated runIllustrative
Extract
crm.accounts12,480 rows
erp.orders96,210 rows
events.api1.2M events
Stage
stg_customersdeduplicated
stg_orderstyped
Test
18 data testsnulls, keys, ranges
Model
fct_revenueincremental
Publish
dashboardrefresh
assistantre-index
Data engineering process
How it runs.
We start by finding out how data really moves, then fix the flows that cost you most, one slice at a time.
- 01
Diagnose
2–3 weeksWe trace every source, owner and flow, and find what is slowing decisions down.
You get
- A map of every source, owner and data flow
- Quality issues ranked by business cost
- A costed, prioritised plan
- 02
Design
1–2 weeksWe design a warehouse or lakehouse sized to your team and cloud, and agree the order things ship in.
You get
- Target architecture with every decision written down
- A roadmap that ships value every few weeks
- Fixed scope and cost for the first pipeline
- 03
Build
6–10 weeks per slicePipelines are modelled, tested and documented as they are built, then released in production slice by slice.
You get
- Tested pipelines and models in production
- Data quality checks and alerting on every flow
- Your engineers pairing with ours
- 04
Operate
Ongoing, then handoverWe run the platform with you, watch it in production, and hand over when your team is ready to own it.
You get
- Monitoring and alerts on every pipeline
- Runbooks and training for your team
- A clean handover, on your timeline
How it runs
A typical engagement.
Value early, then the full platform. Each stage is scoped and priced before it starts.
Diagnose
Sources, flows, owners and pain points mapped.
First pipeline in production
One high-value flow, end to end, tested.
Platform build-out
Remaining sources, models and governance.
Handover
Runbooks, training and on-call moved to your team.
Durations are typical, in weeks
Wk 06121824
Questions
What CTOs ask us.
No. We often work on the platform you already pay for. If a move makes sense, we show the cost and benefit first, and migrate in slices so reports keep working throughout.
That is how we prefer to work. Our engineers pair with yours, follow your review process, and leave the code and the knowledge in your repositories.
We work inside your cloud accounts under your access policies, mask personal data in non-production environments, and document who can see what. We work within the compliance requirements you already operate under, and agree them with you before any work starts.
Most of our warehouse work has been in retail, consumer goods, pharma and education, and we build on AWS, Azure and Google Cloud. The patterns carry across industries, because the hard part is usually the data, not the sector.
You keep everything: code, infrastructure-as-code, documentation and runbooks. We train your team and can stay on a light support retainer if you want one.
Contact
Book a free 15-minute call to talk through what you are working on.
How we connect
- 01Book a short call
Choose a time that works for you on our calendar.
- 02Discovery call
A short call to understand the problem, the people and the constraints.
- 03Final scope and kickoff
We send a clear scope and cost. Once you agree, we start.
Office
393, WeWork Vijohn Tower, 3rd FloorUdhyog Vihar Phase-4, GurugramHaryana 122016
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