Data Engineering

Building a data warehouse takes time. People start it, then leave. New people join and build on top, or start their own pipelines in their own way. The result is no structure, a patchwork of tools stitched together, and pipelines nobody wants to touch because they're not documented and nobody knows how they work.

The problem

Pipelines break silently and nobody notices until a stakeholder asks why the dashboard hasn't updated since last month

Your warehouse has 200+ models and zero documentation, so nobody wants to touch them

Every data engineer builds pipelines their own way, creating a patchwork of styles and tools

People leave and take the knowledge of how things work with them

Every quick fix adds another layer of tech debt that compounds monthly

Your data team spends 70% of their time on plumbing and 30% on actual analysis

How we solve it

We build data pipelines from scratch, or fix what you have. Either way, the result is the same: minimal code, clear documentation, and models that are self-explanatory. Any engineer on your team should be able to understand and modify any pipeline without fear.

We set up alerts that live where your team lives, be it Slack or anywhere else. When a pipeline breaks, you find out the minute it happens, not a week later when a stakeholder asks why the numbers look wrong.

We implement modern data infrastructure with the tools that fit your stack: dbt for transformation, Fivetran or Airbyte for ingestion, and Snowflake, BigQuery, Databricks, or Redshift for storage. Modular, tested, documented, and handed off so your team can run it independently.

What you get

Clean, documented pipelines

Minimal code that's self-explanatory. Any engineer can understand and modify any pipeline without fear. No more "nobody wants to touch this" situations.

Real-time break alerts

Alerts that live where your team lives. When a pipeline breaks, you know immediately, not when a stakeholder complains a week later.

End-to-end data architecture

From ingestion to transformation to storage. Modular layers that are easy to extend, not a monolith that's impossible to change.

Automated data quality tests

Freshness monitoring, anomaly detection, and schema change alerts. Your team trusts the data because the tests catch problems before anyone sees them.

Knowledge transfer

Documentation and handover so your team runs it independently. When someone leaves, the knowledge stays.