Service / Data & analytics

Data systems people can debug when numbers move.

We build data platforms, pipelines, semantic layers, migrations, and dashboards with the same operating discipline we apply to infrastructure and software.

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What gets fixed

Make data trustworthy enough for operations and decisions.

A dashboard is only useful if the team can explain how the number got there. We focus on lineage, freshness, contracts, observability, and the boring workflows that make data reliable.

We help teams build ingestion paths, transformation layers, migration plans, metrics definitions, and operational dashboards that can be tested and repaired like any other production system.

The goal is not another reporting surface. The goal is a data system your engineers, analysts, and operators can reason about together.

Outcomes

Data work that behaves like engineering work.

Your team should leave with tested flows, named owners, visible freshness, and fewer arguments about which number is real.

01

Traceable data

Important numbers can be followed back to their source, transform, and owner.

02

Reliable pipelines

Freshness, quality, and failure signals are visible before stakeholders notice drift.

03

Shared definitions

Metrics and business terms are defined once, reviewed, and reused across surfaces.

04

Operational handoff

Runbooks explain how to backfill, recover, validate, and evolve the data system.

Related work

Data systems often connect to software and AI work.