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.
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.