Services

ETL & ELT pipelines.

We design ETL and ELT pipelines that ingest, clean and reconcile data into one trustworthy store — idempotent, observable and tested. NZ-based engineers.

Most data problems are really pipeline problems: feeds arrive late, in odd shapes, and quietly disagree with each other. We build the ingestion and transformation layer that pulls those sources together, cleans and reconciles them, and lands one version everyone can trust — idempotent on re-run, observable when it breaks, and tested where it counts.

Where we help

Batch ingestion from APIs, files, SaaS exports and operational databases
Change-data-capture and incremental loads instead of full re-pulls
ELT transformations in the warehouse with dbt — versioned and tested
Deduplication, validation and reconciliation so figures actually tie out
Idempotent, restartable jobs that survive partial failures and replays
Orchestration, scheduling and dependency-aware backfills
Data-quality checks, freshness SLAs and alerting on broken feeds
Documented lineage from raw source through to the reporting table

Example builds

End-to-end data platform. Take raw, awkward feeds all the way through to clean, queryable tables.

Reconciled ingestion. Pull from several sources, validate and gap-fill, then reconcile the differences before anything downstream sees them.

Reporting pipeline. An automated path from source to scheduled output — collect, transform, deliver, on time, every run.

How we engage

We model the transformations as code: dbt for in-warehouse ELT, version control, and tests on the assumptions that matter — uniqueness, freshness, referential integrity — so a bad load fails loudly instead of poisoning a report. PostgreSQL, Snowflake and ClickHouse are our usual destinations, and we instrument every job so you can see what ran, what it touched, and what it cost. Single source, plural truths: one pipeline, many trustworthy views. See our services for how we scope and price.

Common questions

ETL or ELT — which should we use?: Usually ELT when you land in a modern warehouse: load raw, then transform in-database with dbt where it is cheap, testable and reproducible. ETL still wins when you must clean or mask data before it is stored at all. We pick per source rather than dogmatically.
Can you work with our existing data stack?: Yes. We integrate with whatever you run — Postgres, Snowflake, ClickHouse, your current orchestrator — and add the pipelines, tests and lineage rather than insisting on a rebuild.
How do you stop pipelines breaking silently?: Freshness and volume checks, schema-drift detection and alerting on every feed, plus idempotent jobs so a re-run is safe. A broken load surfaces immediately instead of showing up as a wrong number weeks later.

Data architecture Warehouses & lakehouses Streaming & event sourcing

Considering this for a project?

Start a conversation

Where we help

Batch ingestion from APIs, files, SaaS exports and operational databases
Change-data-capture and incremental loads instead of full re-pulls
ELT transformations in the warehouse with dbt — versioned and tested
Deduplication, validation and reconciliation so figures actually tie out
Idempotent, restartable jobs that survive partial failures and replays
Orchestration, scheduling and dependency-aware backfills
Data-quality checks, freshness SLAs and alerting on broken feeds
Documented lineage from raw source through to the reporting table

Example builds

End-to-end data platform. Take raw, awkward feeds all the way through to clean, queryable tables.

Reconciled ingestion. Pull from several sources, validate and gap-fill, then reconcile the differences before anything downstream sees them.

Reporting pipeline. An automated path from source to scheduled output — collect, transform, deliver, on time, every run.

How we engage

Common questions

ETL or ELT — which should we use?

Usually ELT when you land in a modern warehouse: load raw, then transform in-database with dbt where it is cheap, testable and reproducible. ETL still wins when you must clean or mask data before it is stored at all. We pick per source rather than dogmatically.

Can you work with our existing data stack?

Yes. We integrate with whatever you run — Postgres, Snowflake, ClickHouse, your current orchestrator — and add the pipelines, tests and lineage rather than insisting on a rebuild.

How do you stop pipelines breaking silently?

Freshness and volume checks, schema-drift detection and alerting on every feed, plus idempotent jobs so a re-run is safe. A broken load surfaces immediately instead of showing up as a wrong number weeks later.