Data Engineering
Pipes that don't wake you up.
Ingestion, warehousing, orchestration, and the dbt layer everything else rests on.
Built to scale. Documented to stay.
Data Engineering · live signal
What this engagement looks like by the numbers
0%
avg query speedup
$0.0M+
infra costs saved
0+
pipelines shipped
Problems we solve
If any of these sound familiar, we can help.
What we build
The deliverables.
Data ingestion
Managed connectors (Fivetran, Airbyte, Hevo) + custom pipelines for the weird ones.
Data warehousing
Snowflake, BigQuery, or Databricks — configured, partitioned, and governed.
Data orchestration
Airflow or Dagster for reliable, observable scheduling.
Data transformation
dbt as the single source of truth for business logic.
Engagement models
Three ways to work together.
01
Project-based
Fixed-scope delivery with clear milestones.
Cadence
- Week 1Discovery + audit
- Week 2Architecture + design
- Week 3-5Build + parallel-run
- Week 6-7Hardening + handoff
- dbt from scratch
- Migrate from legacy warehouse to modern stack
- Build ingestion pipelines for 10 new sources
02
Fractional / Retainer
Ongoing engineering capacity for teams without a full-time engineer.
Cadence
- WeeklySprint + demos
- OngoingPipeline ops + monitoring
- As neededNew source integrations
- QuarterlyArchitecture review
- Pipeline maintenance and monitoring
- New source integrations as needed
- Architecture guidance and optimization
03
Advisory
Senior oversight for internal teams.
Cadence
- OngoingAsync architecture reviews
- WeeklyCode review rotation
- MonthlyTeam coaching session
- QuarterlyRoadmap check-in
- Architecture reviews
- Code reviews
- Best practice implementation
Tools we use
The toolbox.
FivetranHHevoAirflowSnowflakeBigQueryDatabricks
Sigma