Data Warehousing & Analytics
A warehouse and model that answer the questions the business actually asks, at a cost you can predict.
Read moreData & AI
Data Engineering & Pipeline Development
Pipelines that run reliably, fail loudly, and can be explained to whoever asks where a number came from.
Overview
A data pipeline nobody trusts is worse than no pipeline, because decisions get made on it anyway. Trust comes from unremarkable engineering: idempotent jobs, validation at ingestion, explicit schema handling and alerting when a source goes quiet.
We build batch and streaming pipelines with tests, lineage and monitoring, using tooling proportionate to your team — which for most New Zealand organisations means managed services and orchestration rather than a bespoke platform.
What you get
Reruns are safe
Idempotent jobs, so recovering from a failure does not duplicate or corrupt data.
Failures are loud
Freshness and volume checks that alert when a source stops delivering, rather than silently producing stale reports.
Quality checked at entry
Schema and business-rule validation at ingestion, so bad data is caught before it spreads downstream.
Lineage documented
A clear path from any figure back to its source, which is what auditors and sceptical executives both ask for.
How we work
- 01
Map
Sources, consumers, update frequency and quality expectations documented with the people who rely on them.
- 02
Design
Batch or streaming chosen per source on genuine latency requirements rather than on preference.
- 03
Build
Pipelines implemented with tests, validation and orchestration, deployed through your normal pipeline.
- 04
Monitor
Freshness, volume and quality metrics with alerting, plus documentation for the team who will own it.
Common questions
Do we need streaming?
Usually not. Most business reporting is entirely well served by hourly or daily batch, which is far cheaper to build and operate. Streaming is worth it when decisions genuinely happen in seconds.
Which orchestration tool?
Airflow, Dagster and cloud-native schedulers are all reasonable. For smaller teams, a managed scheduler is often enough and avoids a platform to maintain.
Can you work with our existing warehouse?
Yes. We work with BigQuery, Snowflake, Redshift, Synapse and plain PostgreSQL, and we will not recommend replacing one that is working.
Often paired with
Database Migration Services
Moving databases to managed services with replication, verified data integrity and minimal downtime.
Read moreMLOps & AI Infrastructure Consulting
Infrastructure, pipelines and evaluation for AI systems — including telling you when you do not need one.
Read moreReady to talk about data engineering & pipeline development?
We will tell you what we would do, roughly what it costs, and whether it is worth doing yet.