Pipelines as software
Version control, code review, CI and environments. No hand-edited jobs on a server.
Data Analytics
Data engineering
ELT pipelines, orchestration and data quality engineering that keep every downstream number trustworthy.
What we do
Data engineering is the unglamorous layer everything else depends on: ingestion from your systems, transformation into clean models, orchestration that runs on schedule and quality gates that stop bad data before it reaches a dashboard. We build and run it with the same rigor as production software, because for your reporting that is exactly what it is.
Pipelines are code: dbt for transformation, Airflow or Dagster for orchestration, Fivetran or Airbyte for ingestion, everything in git with CI, tests and review. An engagement typically starts by stabilizing what you have, then extends it source by source, with tests landing alongside each model rather than in a cleanup phase that never comes.
What changes for your team: mornings stop starting with a broken dashboard and a Slack thread of guesses. Failures alert the right person with lineage attached, incidents have runbooks, and adding a source becomes a routine pull request instead of a fragile side project. Data quality turns from a feeling into a number you can put an SLA on.
Why it pays off
Quality gates built in
Freshness, volume and schema tests run on every load and block bad data early.
Failures that explain themselves
Alerts carry lineage and context, so the fix takes minutes, not a morning of archaeology.
Costs that scale sanely
Incremental models and right-sized warehouses keep compute bills proportional to value.
A stack your team can hold
Boring, documented, widely adopted tools your future hires will already know.
Data Analytics
How we work
01
Stabilize
We instrument what you have with tests, alerting and lineage, so the ground stops moving.
02
Standardize
Ingestion, transformations and orchestration converge on one reviewed, documented pattern.
03
Extend
New sources and models are added in priority order, each with tests and documentation as a merge requirement.
04
Operate and hand over
On-call, runbooks and dashboards for the pipelines themselves, then a staged handover to your engineers.
Deliverables
ELT pipelines in git with CI
Orchestration with retries and alerting
Data quality test suite and SLAs
Lineage and pipeline documentation
Incident runbooks and handover
Questions, answered
Our pipelines break weekly. Rebuild or repair?
Usually repair first. Tests and alerting around the existing pipelines stop the bleeding in weeks; rebuilds happen incrementally where the audit shows they pay off. A big-bang rewrite is rarely the answer.
Fivetran or custom ingestion?
Managed connectors for the standard sources, custom code only where APIs are unusual or volumes make per-row pricing hurt. The audit includes that per-source calculation, with numbers rather than preferences.
How do you define data quality?
As tests with owners: freshness, volume, schema, uniqueness and business rules, checked on every run. Quality is a number on a dashboard and an alert with a name on it, not a slogan.
Make the pipelines boring
Tell a senior engineer where it hurts and get a stabilization plan measured in weeks.
Book a discovery call