Data Engineering
Pipelines, warehouses, and platforms that turn raw data into business-ready assets.
A modern data platform isn’t a vendor — it’s a system
We don’t sell you “the Modern Data Stack” as a religion. We design what your team needs to operate confidently:
- Ingestion that handles your real sources — APIs with bad uptime, CDC from operational DBs, batch dumps, event streams. Whatever’s there.
- Transformation in dbt, with tests as gates, docs auto-generated, and a semantic layer so finance and product agree on what “active user” means.
- Orchestration via Airflow or Dagster — DAGs that retry sensibly, alert when SLAs slip, and don’t wake up your oncall at 3am for transient failures.
- Observability at every layer: freshness, volume, schema drift, row counts vs expectations.
- Cost discipline: warehouse sizing, query review, materialisation strategy. Every Snowflake customer overpays until someone makes this their job.
When this fits
Your team is exporting CSVs to make a report. Or your nightly ETL takes 9 hours and breaks every other week. Or analysts can’t trust dashboard numbers because the same metric has three definitions. These are data-engineering problems, not BI tool problems.
Questions about Data Engineering.
With a 1–2 week discovery: we audit current sources, map what downstream teams actually use, and propose a phased migration. The first deliverable is usually a single high-value pipeline (e.g., revenue or customer health) so you see value before committing to a full platform.
Depends on your workload mix. Snowflake wins for pure SQL analytics and ease of use. Databricks wins when you have ML/Spark workloads sitting next to analytics. BigQuery wins on GCP-native shops and for true serverless economics. We make this call based on your stack, not preferences.
dbt tests on every model (unique, not_null, relationships, accepted_values), plus business-logic tests we write together. We layer on freshness checks via Airflow sensors and column-level monitoring via tools like Elementary or Monte Carlo when budgets allow.
Yes. Once the warehouse is the source of truth, syncing curated audiences and metrics back to operational tools (Salesforce, Hubspot, Intercom, etc.) is the natural next step. We use Hightouch or Census, or hand-rolled pipelines for simpler cases.
Ready to talk about Data Engineering?
Tell us about your project. We respond within 24 hours.
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