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Data Warehousing

One set of numbers the whole room agrees on

We build the governed layer that sits between your operational systems and everyone who asks questions of them — modelled properly, loaded on a schedule you can trust, and documented so a definition means the same thing in every report. Snowflake builds and ETL pipelines, designed for the questions you will ask next year as well as the ones on your desk now.

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A senior engineer replies within 24 hours — not a sales rep.

The signs your reporting layer has outgrown itself

Three teams walk into a meeting with three revenue figures

Finance pulls from the billing system, sales pulls from the CRM, the ops report comes out of a warehouse extract someone built two years ago. All three are defensible. None of them match. The next forty minutes go on reconciling numbers instead of deciding anything, and that happens every month.

Nobody can say what a metric actually counts

Ask five people what an active customer is and you get five rules — logged in this month, has an open subscription, has spent anything in ninety days. Each version is hard-coded somewhere different. When a definition changes, someone has to remember every place it was written down, and they never do.

The load broke on Tuesday and you found out on Friday

Pipelines run on a scheduler nobody watches. A source system changed a column name, the job failed silently or half-loaded, and a week of decisions were made on stale data. There is no freshness check, no row-count assertion, and no alert that reaches a human who can act on it.

Every new question means another one-off extract

The analytics team spends its week writing bespoke SQL against production replicas because the model does not support the cut anyone wants. Those extracts become load-bearing, they never get retired, and now your reporting depends on a folder of scripts on one laptop.

How we build a warehouse that holds up

Warehouses fail on modelling and definitions far more often than on infrastructure. We settle what the numbers mean with the people who own them, then build to that agreement.

Definitions agreed before a single table is designed

We run the metric conversation early, with finance and the business owners in the same session, and write the outcome down as a governed set of definitions. That document becomes the specification for the model. It is unglamorous work and it is the difference between a warehouse people trust and another data source to argue with.

Modelled for the questions after this one

We design at the grain of the business event — an order line, a shipment, a claim — using dimensional models with proper conformed dimensions and history where it matters. Handling slowly changing attributes correctly is what lets you ask what a customer's segment was at the time of purchase, rather than what it happens to be today.

Pipelines that fail loudly and recover cleanly

Loads are idempotent, so a rerun after a failure does not double your revenue. Every stage carries tests — row counts, referential checks, freshness thresholds — and a breach raises an alert with the failing assertion named, not a generic job-failed email. Reprocessing a bad day is a documented operation, not an emergency.

Lineage, access and cost you can see

Column-level lineage so an analyst can trace a figure back to the source field that produced it. Role-based access separating who reads sensitive columns from who reads aggregates. And warehouse compute broken out by workload, because consumption pricing punishes teams who cannot tell which query is expensive.

What this covers

Pick the piece you need, or bring us the problem and we'll tell you which applies.

Snowflake Development

Snowflake done properly means treating compute and storage as separate levers rather than one bill. We design the warehouse layout, the role hierarchy and the workload separation that keeps a heavy transformation job from starving the dashboards, and we build the transformation layer on top of it. Suited to teams whose data volume or concurrency has outgrown a single database server.

Database, schema and role hierarchy with least-privilege grants mapped to real job functions
Separate virtual warehouses per workload, with auto-suspend and sizing tuned against actual query profiles
Streams and tasks for incremental loading, plus Time Travel and zero-copy clones for safe testing against production data
Query profiling and clustering decisions driven by the credit consumption you can see per workload

ETL & Data Pipeline Development

The pipelines that get data out of your source systems, reshape it and land it somewhere reliable. Most of the difficulty is not the transformation logic — it is change data capture, late-arriving records, schema drift and the API that rate-limits you at the worst moment. We build for those cases rather than the happy path.

Incremental extraction with change data capture, so nightly full reloads stop being the only option
Orchestrated dependency graphs with retries, backfills and per-task alerting instead of one monolithic scheduled script
Schema-drift handling that quarantines unexpected changes rather than failing the whole run
Data quality assertions at each stage, with a rejected-records store an analyst can actually inspect

The stack we build on

Chosen to fit the problem — not because it's what we used last time.

Warehouse platforms

Snowflake
Amazon Redshift
Google BigQuery
Azure Synapse Analytics
PostgreSQL
SQL Server

Ingestion & transformation

dbt
Apache Airflow
Azure Data Factory
SSIS
Fivetran
Apache Kafka
Debezium

Storage & processing

Amazon S3
Azure Data Lake Storage
Apache Spark
Databricks
Parquet
Delta Lake

Engineering & operations

Python
SQL
Terraform
Docker
GitHub Actions
Great Expectations
Grafana

How we'll work together

Every stage ends with something in your hands — not a status update.

01
Source inventory and metric agreement
We catalogue every system that holds numbers anyone reports on, profile the data inside them, and run the sessions that settle contested definitions. Where two departments genuinely need different cuts, we name both rather than forcing a compromise nobody uses.
You get: A source system inventory with data profiles, a signed-off metric definition set, and a target model scope.
02
Model design and a proof load
We design the dimensional model, then load a meaningful slice of real data into it and reconcile that slice against your existing reports. Disagreements found here are cheap. Disagreements found after go-live cost you the credibility of the whole platform.
You get: A dimensional model with documented grain and keys, a populated proof environment, and a reconciliation report against current reporting.
03
Pipeline build and hardening
Extraction, transformation and orchestration built incrementally, with tests written alongside each stage. We deliberately break things in staging — kill a job mid-run, feed it a renamed column — to confirm recovery works before you depend on it.
You get: Version-controlled pipelines and transformation code in your repositories, a test suite, and alerting wired to your on-call channel.
04
Parallel run, then handover
The new warehouse runs alongside the old reporting for a period so both sets of numbers can be compared on live data. Once your team is signing off on the new figures, we retire the legacy extracts and train your analysts on the model.
You get: A comparison log from the parallel run, model and lineage documentation, and a working session covering pipeline operation and backfills.

Where we've applied this

Retail & e-commerce

Orders, returns, stock movements and marketing spend joined at a common customer key, so channel profitability stops being a quarterly spreadsheet exercise.

Logistics

Consignment and telemetry events modelled at the shipment leg, letting you measure carrier performance and dwell time on the same basis across every route.

Finance

Transaction and ledger history with full audit lineage, immutable snapshots and access separation between analysts and staff who may see account-level detail.

Healthcare

Clinical, scheduling and billing data brought together with de-identification applied in the pipeline, so analysts work on governed extracts rather than production copies.

Manufacturing

Production, quality and maintenance records aligned to a common asset and batch dimension, which is what makes yield analysis across plants comparable at all.

SaaS platforms

Product usage events reconciled with subscription billing, so retention and expansion figures come from one place instead of an analytics tool and an invoice ledger.

Clients we've built for

Real products, in production, with real users on them.

National Industries Company

evera

Prime Selection

Beat22

Questions buyers ask us

What drives the cost of a data warehouse build?

Mostly the number and awkwardness of your source systems. A clean REST API is a day; a legacy ERP with no documented schema and a nightly export window is weeks. After that it is how much disagreement exists about definitions, because that is people-time, not engineering time. Volume matters least — modern platforms handle far more data than most mid-sized businesses generate.

How long until we can report off it?

We aim to have one subject area — usually sales or finance, whichever is causing the most arguments — modelled, loaded and reconciled within the first couple of months. Additional domains land faster because the conformed dimensions already exist. Building the whole estate before anyone gets a usable report is how these projects lose their sponsor.

Should we use Snowflake, BigQuery or something we already own?

If your data already sits in one cloud, staying there usually wins on egress cost and integration effort alone. Snowflake earns its place when you have several teams with genuinely different workloads and want to isolate their compute and spend. And if your volumes are modest, a well-modelled PostgreSQL instance is a legitimate answer that we have recommended more than once.

Can you work with the pipelines and reports we already have?

Yes, and we would rather extend than replace where the existing work is sound. A common shape is that we take over orchestration and modelling while your team keeps owning the source extracts they know best. Where legacy SSIS packages or hand-written scripts still do their job, they can feed the new model until there is a reason to rewrite them.

Who does this work and can our engineers be involved?

Data engineers who have built and operated warehouses, not generalists learning on your project. Everything goes through pull requests in your repositories, so your team reviews the model and the transformation code as it lands. Where you have analysts who write SQL, we usually train them on the model during the build so they are productive on day one rather than waiting on us.

What happens after the build — do we need you to run it?

No. The warehouse lives in your cloud account, the code is in your repositories, and the runbooks cover backfills, schema changes and load failures. We hand over with a working session rather than a document drop. Ongoing support is available if you would prefer a retained team for pipeline changes and new subject areas, but it should be a decision rather than a necessity.

Tell us which number is being argued about

Send us a short note on where your reporting disagrees with itself and which systems the figures come from. A senior data engineer replies within one business day with a straight read on what modelling that properly would involve.

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