Data Platform Engineering
We build data pipelines and warehouses using tools that have been around long enough to trust: Airflow, dbt, PostgreSQL. Nothing exotic, nothing that needs a rewrite in two years.
Why teams call us
Most data platform problems aren't technical — they're a lack of visibility
If you only find out something's wrong when a customer complains, the platform isn't doing its job. That's usually a monitoring and testing gap, not a "rewrite everything" problem.Pipelines break, someone finds out from a user
By the time it gets reported, the bad data has already spread downstream
No one really knows what will happen if a source changes
The pipeline was built for a specific case, and it shows when the data doesn't fit
The last platform aged badly
Built on a framework that was popular for a year, now unmaintained and hard to hire for
What we actually build
ETL/ELT pipelines
Airflow DAGs with retries and error handling, so a failed step doesn't require someone to notice and re-run it manually
Transformations in dbt
SQL models your team can read and modify without us — that's the point of dbt over a black-box script
Warehouses on PostgreSQL / TimescaleDB
Sized and indexed for your actual query patterns, not a generic best-practices template
Monitoring with Grafana + Prometheus
Alerts on the things that actually indicate a problem, tuned to avoid alert fatigue
Data quality tests
dbt tests that catch bad data before it reaches a dashboard someone makes a decision from
Multi-source integration
Reconciling data from systems that were never designed to talk to each other
How we approach it
Design for partial failure
One source going down shouldn't take the whole pipeline with it. We build in degraded modes, not just all-or-nothing.
Pick tools your team can actually run
PostgreSQL, Airflow, and dbt are boring on purpose. Boring means your team can hire for it and debug it at 2am.
Test the data, not just the code
A pipeline can run successfully and still produce garbage. dbt tests check the output, not just that the job finished.
Leave you with something your team owns
Documentation and a handoff, not a system only we understand. We'd rather lose the maintenance contract than leave you stuck.
Case study: integration monitoring platform
Turning partner integration issues from support tickets into alerts
A logistics client's integration layer handles 2.7M messages a day across 672 partner connections in 4 regions. Before this project, failures showed up as support tickets. Now they show up as alerts, before a customer notices. Full details in the case study.Scale
2.7M messages/day, 672 partner connections, 4 regions
Starting point
Partner data integration issues were surfacing as customer complaints, not alerts
What we built
Airflow + dbt + PostgreSQL for the pipeline, Grafana + Prometheus for monitoring
What changed
Issues get caught by monitoring before they reach a customer, instead of after
Tools we use
Pipeline orchestration
Data transformations
Data warehouse
Time-series data
Operational dashboards
Metrics & alerting
Data processing
Source systems
Scope of work
Timeline depends on scope
How long it takes depends on how many sources you have, how messy the data is, and how much needs historical backfill. We'll give you a real timeline after we've looked at your setup, not before.Single integration
One source, one destination, done well
Multi-source platform
Several sources feeding a shared warehouse and monitoring setup
Larger, multi-region setup
Multiple sources across regions, higher volume, more moving parts
Want to talk through your setup?
First call is free and there's no pitch deck. If it's not a good fit, we'll tell you that too.