Incremental, not full reloads
Sized to your data volume, so runs stay fast as history grows.
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ETL & ELT pipelines
ETL and ELT pipelines built for data that will not sit still.
We build the pipelines that move your data from wherever it lives into a warehouse where it can be trusted: incremental, orchestrated, tested, and re-runnable without a person babysitting them.
- Incremental loads
- Airflow orchestration
- dbt transforms
What you get
Pipelines that run without a babysitter.
Retries that can't corrupt
Idempotent, atomic writes. Partial loads never reach reporting.
History that stays correct
Trailing restatement windows re-pulled on every run.
Backfills your team can run
Runbooks and backfill commands at handover.
The problem
The pipeline that works until it matters.
Most reporting breaks in the same three places. A full-table reload that took four minutes in year one now takes six hours. A job fails at 2am, retries into a half-written table, and nobody notices until a client asks why revenue dropped. And history quietly changes underneath you, because ad platforms and marketplaces restate conversions for days after the fact. A pipeline that assumes yesterday is final will be wrong every single day.
What we build
What's included.
- Incremental extract and load pipelines sized to your data volume, not full reloads
- ELT patterns that land raw data first and transform inside the warehouse, so a bad transform never costs you the source
- Restatement windows that re-pull the trailing period ad platforms are still changing
- Orchestration in Airflow or Dagster with dependencies, retries and idempotent tasks
- dbt transformation layers with tests, so a null or a duplicate fails the run instead of reaching a dashboard
- Backfill tooling your team can run themselves when history needs to be rebuilt
How we work
How we run it.
Step 1
Trace the current flow source by source and find where numbers actually diverge
Step 2
Land raw ingestion first, so nothing depends on a transform being right on day one
Step 3
Model and test the reporting layer in dbt, then cut dashboards over channel by channel
Step 4
Add alerting and hand over runbooks and backfill commands
What we use
Tools, platforms and methods.
- Airflow
- Dagster
- dbt
- Snowflake
- BigQuery
- Databricks
- Python
FAQ
Common questions.
What is the difference between ETL and ELT, and which do you use?
ETL transforms data before it lands; ELT lands it raw and transforms inside the warehouse. We default to ELT because warehouse compute is cheap, raw history means you can rebuild any model without re-pulling APIs, and transformation logic in dbt is testable and reviewable. We use ETL where compliance requires filtering or masking before the data ever lands.
How real-time can a marketing pipeline realistically be?
Bounded by the source, not by us. Most ad platform reporting APIs update hourly at best and restate for days, so hourly ingestion is genuinely current. Event streams like server-side conversions can run in near real time. We are direct about which of your sources can support a live number and which cannot.
How do you stop a failed job from corrupting the warehouse?
Every task is idempotent and writes atomically: loads go to a staging location and swap in only on success, so a retry produces the same result as a clean first run. Partial writes never become visible to reporting.
Can our analysts change the pipeline after handover?
That is the goal. Transformations live in dbt as version-controlled SQL, which most analysts can already read and extend. We hand over the repo, the tests, the runbooks and a working local setup.
Go deeper
Guides from our engineers.
Guide
ETL Pipelines for Amazon Sellers: How to Unify SP-API, Ads API, and Inventory Data
Amazon sellers juggle data across SP-API, the Ads API, and spreadsheets. Here's how a proper ETL pipeline unifies it into one reliable source of truth.
Read the guideGuide
How to Build a Marketing Data Warehouse with dbt and Snowflake
A practical guide to building a marketing data warehouse with dbt and Snowflake: architecture, modeling layers, metric definitions, and the mistakes that sink these projects.
Read the guideRelated
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Learn moreTalk to an engineer.
Tell us where your data lives and where the numbers diverge. We'll tell you what we'd rebuild first.