Data engineering & pipelines
dbt, Fivetran, Airbyte, Snowflake, BigQuery — warehouses that work.
Data engineering & pipelines is dbt, fivetran, airbyte, snowflake, bigquery — warehouses that work.
Why this work matters
Most analytics fail at the data layer: pipelines silently fail, transformations drift, definitions vary across tools, and the warehouse bill triples in 6 months because nobody monitors compute. We build foundations that don't.
The work, in detail.
- Snowflake, BigQuery, Databricks, Postgres
- dbt models + tests + docs
- Fivetran, Airbyte, Stitch ingestion
- Reverse ETL (Hightouch, Census)
- Data quality + freshness monitoring
- Cost optimization (compute + storage)
- Governance (lineage, PII, access)
- →Modeled warehouse (raw → staging → marts)
- →dbt project with tests + docs
- →Pipeline orchestration
- →Data quality + freshness monitoring
- →Cost dashboards
We build the data foundation that makes BI, ML, and analytics possible — pipelines, warehouses, transformations, and governance. Without the consultancy markup.
The approach.
Modeled, not dumped
Raw → staging → marts via dbt with tests on every model. Every dimension and metric has one owner and one definition.
Cost-aware
Warehouse cost is engineering's responsibility. Cluster keys, partition strategies, materialization choices, and dashboard compute caps — designed in, not bolted on.
Governed by default
PII tagging, role-based access, lineage, and column-level docs. Audit prep takes days, not months.
Data engineering & pipelines — common questions
What does a data engineering engagement deliver?
A modeled warehouse moving raw to staging to marts, a dbt project with tests and docs, pipeline orchestration, data quality and freshness monitoring, and cost dashboards. The aim is a foundation that doesn't silently fail or drift over time.
Which platforms and tools do you work with?
Warehouses including Snowflake, BigQuery, Databricks, and Postgres; dbt for models, tests, and docs; Fivetran, Airbyte, and Stitch for ingestion; and Hightouch or Census for reverse ETL. We also handle data quality and freshness monitoring and governance for lineage, PII, and access.
Why do analytics projects fail at the data layer, and how do you prevent it?
Pipelines silently fail, transformations drift, definitions vary across tools, and the warehouse bill triples because nobody monitors compute. We model raw to staging to marts via dbt with tests on every model, and give every dimension and metric one owner and one definition.
How do you keep warehouse costs under control?
We treat warehouse cost as engineering's responsibility, not an afterthought. Cluster keys, partition strategies, materialization choices, and dashboard compute caps are designed in rather than bolted on, and cost dashboards ship as a deliverable.
Is the warehouse audit- and governance-ready?
Yes. We govern by default with PII tagging, role-based access, lineage, and column-level docs, so audit prep takes days rather than months.
More from Data, BI & Power Platform
The cost of waiting
is your competitor.
Every 90 days you delay is 90 days of authority compounding for someone else. Get the audit. See the math. Then decide.