Data Engineering · 02 of 05

Pipelines that don't wake you at 3am.

Cleanly modeled, governed, fast warehouses — with ETL pipelines that explain themselves when something breaks. We design for the analyst querying at 9am, the auditor asking in Q4, and the SRE paged at 3am. All three should find a warehouse they trust.

Plan a warehouse build ↗See the architecture
99.8%Pipeline success rate
−43%Median warehouse spend
6 wkTo a modeled mart
What you get

A warehouse you can defend to anyone.

Six muscles every mature data platform needs — built once, owned forever.

Source ingestion

SaaS apps, prod databases, files, events. Managed connectors where they make sense; bespoke where they don't.

Fivetran · Airbyte · CDC

Dimensional modeling

Kimball-style facts and conformed dimensions. Stable interfaces, slow-changing where they should be, fast queries by default.

Kimball · SCD2 · conformed

Transformation (dbt)

Staging → intermediate → marts in dbt. Modular, tested, version-controlled. Every model owns its tests.

dbt · CI · 100% tested

Data quality & SLAs

Freshness, completeness, uniqueness, accepted-values — baked into the pipeline. SLAs that page the owner, not the team.

freshness · SLA · paging

Governance & lineage

Column-level lineage, owners, deprecation policies, RBAC. The audit story writes itself.

lineage · RBAC · catalog

Performance & cost

Partitioning, clustering, materialized views, query rewrite. Same workload, smaller bill, faster reads.

clustering · materialized · cache
How it works

Five layers, clear contracts, no surprises.

The same reference architecture we've built into Snowflake, BigQuery and Redshift warehouses across 20+ projects.

01 · SourcesSaaSDBs · CDCFilesEvents / Kafka02 · Raw & stagingraw.*stg_*snapshots03 · Modelingint_* · business logicjoin, enrich, dedupefct_* · dim_*conformed marts04 · Quality & governanceTestsSLAsLineageCatalog05 · ConsumersBIReverse ETLML / agents

We make the warehouse a system, not a swamp.

Every model has an owner, every metric a definition, every pipeline a freshness SLA. The platform answers most questions before anyone has to ask.

  • 01
    Source contracts

    Schemas pinned, breaking changes detected at the connector boundary — not in the dashboard at month-end.

  • 02
    Staging is sacred

    Thin, type-safe, idempotent. The only place we do casting and renaming. Easy to reproduce.

  • 03
    Models with tests

    Every model ships with at least four tests — uniqueness, not-null, referential, accepted values.

  • 04
    Marts as the API

    Conformed dimensions and facts — the “public interface” of the warehouse. Versioned, deprecated cleanly.

Tech stack

Picks that survive a CFO review.

Mature, supported, well-documented. The boring choice is usually the right choice in data platforms.

Warehouses

SnowflakeBigQueryRedshiftSynapseDatabricks SQL

Ingestion

FivetranAirbyteStitchMeltanoDebezium (CDC)

Transformation

dbt Cloud / CoreSQLMeshCoalesceDataform

Orchestration

AirflowDagsterPrefectArgo

Quality

Great ExpectationsSodaMonte CarloElementary

Catalog & lineage

AtlanCollateDataHubOpenLineage

Reverse ETL

HightouchCensusPolytomic

Legacy & on-prem

InformaticaTalendSSISOracle DI
From vision to victory

From spreadsheet sprawl to a warehouse, in six weeks.

A five-step rollout that respects your existing investments and retires the brittle ones.

01
Week 1
Inventory & assess

Sources, current pipelines, real costs, real pain. We grade the platform honestly.

02
Week 2
Model design

Conformed dimensions, fact tables, naming. Sketched before a single connector turns on.

03
Week 3–4
Build pipelines

Ingestion → staging → marts in dbt. Test coverage on day one.

04
Week 5
Migrate & cut over

Parallel-run with legacy. Validate row-for-row. Switch traffic with a clean rollback path.

05
Ongoing
Operate & tune

SLAs, cost reviews, deprecation flow. Quarterly platform health report.

Where this fits

Three warehouse jobs we've finished.

Each one a quiet success: pipelines that just run, costs that came down, analysts who stopped escalating.

Pattern · Migration

Off SSIS, onto Snowflake — in one quarter.

Manufacturing client migrated from a Frankenstein SSIS + SQL Server estate to Snowflake + dbt. Same numbers, smaller bill, fewer pages.

−51%Platform spend
0Cutover incidents
SnowflakedbtAirflow
Pattern · Greenfield

A Series B without a data team.

Built the data platform end-to-end for a fast-moving Series B — sources, warehouse, semantic layer, BI — and trained their first hire on the way out.

6 wkZero → exec dashboard
14 srcsModeled
BigQueryFivetrandbt
Pattern · Governance retrofit

A warehouse that became auditable.

Insurance client needed lineage + ownership + RBAC on a 5-year-old warehouse. Retrofit without rewriting — auditor cleared in one cycle.

1,400Models cataloged
SOC 2Cleared
AtlanOpenLineageMonte Carlo
Why ETY

Engineers who've operated the warehouse.

22Warehouses designed or migrated across cloud platforms.
99.8%Median pipeline success rate on operated warehouses.
−43%Median warehouse cost reduction after tuning.
dbt-firstWe default to dbt — but we'll meet legacy stacks where they are.
Continue exploring
Analytics & Visualization

The dashboards and semantic layer that sit on top of the warehouse you build here.

→
ELT & Data Lakehousing

When you need to keep raw data and let multiple engines read it — ELT is the answer.

→

A warehouse that earns its bill.

Send us your three most-used dashboards and your current warehouse cost. We'll come back with a target architecture, a migration plan, and a credible cost line.

Book a discovery call ↗Back to Data Engineering