Data Engineering
Analytics that don't run on a warehouse are slow analytics. Models that don't run on clean data are wrong models. Activation that doesn't run on governed data is risky activation.
We build the data infrastructure — warehouse, pipelines, transformation, quality, activation — that every other analytics initiative downstream silently depends on.
What we do
Does this sound familiar?
Reports take hours to refresh and analysts trust the cache
Your dashboards crawl. Analysts pull extracts because the live data is too slow to query. Half the team works off three-day-old CSVs and calls it good enough.
When storage and compute share a single legacy box, performance degrades linearly with data volume. Every quarter the experience gets worse, every quarter someone proposes 'just adding a node', and every quarter the bill grows faster than the speed.
We migrate teams to modern cloud warehouses — BigQuery, Snowflake, Databricks — with partitioning, clustering, and materialisation tuned to your real query patterns. Sub-second dashboards stop being aspirational.
Diagnosis:On-prem and legacy warehouses scale by invoice; modern warehouses scale by SQL.
Pipelines are duct-taped and break every release
Custom Python scripts on a cron job. A handful of Zapier zaps nobody documented. One analyst who knows where the credentials live. Each upstream schema change quietly breaks something downstream.
Hand-rolled pipelines feel cheap until you count the engineering hours spent maintaining them — and the cost of the decisions made on stale or wrong data while they were broken.
We consolidate ingestion on managed connectors (Fivetran, Airbyte) for the commodity sources, transform with dbt for the version-controlled, tested layer, and reserve custom code for the edges where it earns its keep.
Diagnosis:Hand-rolled pipelines feel cheap until you count the hours spent unbreaking them.
Nobody trusts the numbers in any dashboard
Marketing's CAC doesn't match finance's. Two dashboards report different revenue. Every executive meeting opens with a debate about whose query is correct instead of what the data says.
Without dimensional models and a governed semantic layer, every team computes metrics their own way. The org argues about definitions rather than acting on insight — and trust in data erodes faster than any one fix can rebuild it.
We model the warehouse properly in dbt — facts, dimensions, documented metric definitions — and stand up a semantic layer (LookML, MetricFlow, Cube) that enforces consistency wherever the data is consumed.
Diagnosis:When every team computes the metric their own way, the org argues about data instead of acting on it.
Pipelines break silently and execs find out first
Schema changes upstream silently corrupt a dashboard. Nobody notices for two weeks. By then the CMO has made a quarter's worth of decisions on a wrong CAC and the post-mortem is awkward.
Without tests, freshness checks, and anomaly detection, pipeline failures are discovered by the most expensive humans in the org — usually in a meeting, usually after the damage is done.
We implement data quality testing (dbt tests, Great Expectations), schema monitoring, freshness SLAs, and anomaly alerting wired to Slack or PagerDuty. Breakage is detected in minutes, not in the Monday review.
Diagnosis:Pipeline failures discovered by an executive are the most expensive bugs you'll ever ship.
Your warehouse is a dashboard graveyard
Data lands in the warehouse, gets visualised in a dashboard, and dies there. Sales still works off CRM exports. Marketing still rebuilds audiences inside Meta. The warehouse is a read-only museum of insight nobody operationalises.
Without reverse ETL, the warehouse is purely analytical — and every score, segment, or lifetime value the data team computes stays trapped behind a login the ops team doesn't use.
We implement reverse ETL with Hightouch or Census so warehouse-modelled audiences, scores, and metrics sync directly into Salesforce, HubSpot, Meta, and the rest of your operational stack. The warehouse becomes the source of truth your teams actually act on.
Diagnosis:A warehouse that only feeds dashboards is half a warehouse — activation is where it pays back.
How we run data engineering
Three principles, applied consistently
Modelled
Dimensional models, documented metric definitions, semantic layer — so every consumer of the data reads from the same source of truth. The end of 'which dashboard is right?'
Observable
Data tests, schema monitoring, freshness checks, anomaly alerting. Pipeline failures get caught upstream — not by an executive in a Friday meeting.
Activated
The warehouse syncs back to the operational tools your teams use. Insight that ships, not insight that decks.
Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.
Frequently asked questions
Data engineering, demystified
All three are excellent. BigQuery integrates well with Google's marketing stack and GA4. Snowflake is strong on multi-cloud and data sharing. Databricks leads on ML and lakehouse architectures. We pick based on your stack, team skills, and workloads.
For modern data modelling, effectively yes. dbt has become the standard for warehouse-native transformation: version-controlled SQL, testing, documentation, lineage. We've yet to encounter a project where it wasn't the right call.
A minimum-viable warehouse with source replication, dimensional models for core domains, and dashboards usually ships in 8-12 weeks. Full programme buildout (semantic layer, ML, activation) extends over quarters — incrementally, not big-bang.
Warehouse costs creep when nobody is watching. We implement cost dashboards, query optimisation, partition / cluster strategies, materialisation rules, and FinOps reviews — so the warehouse is performant AND cost-controlled.
We build, ship, and hand over with documentation, runbooks, and CI/CD. We can run ongoing operations as a managed service or partner with your data team. Either model is fine.
Ready to start with data engineering?
Tell us where you are today and what you're trying to fix. We'll show you exactly how we'd plan, execute, and measure.
- No commitment required
- Speak to a senior architect
- Get a rough timeline estimate
