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DATA ARCHITECTURE & TECHNICAL CONSULTING

Design the pipeline once, run it forever

We help your team design data workflows, choose storage and compute options, and build automated pipelines that scale as your datasets grow.

Why teams need this

Many institutional data problems start as a spreadsheet and end as a fragile batch script run by one person. When that person is on leave, the dataset stops updating. When a vendor changes a column, the job breaks silently. When compliance asks where a number came from, the answer is a folder full of CSVs with no naming convention.

Our architecture service is the fix. We work with your engineers (or alongside your existing IT vendor) to design a data workflow that is repeatable, observable and documented — ingestion, storage, transformation, validation, publication and monitoring — without over-engineering it for the size of the dataset you actually have.

We are deliberately technology-agnostic. We have built workflows on Python, DuckDB, Postgres, cloud object storage, scheduled compute and on-premise servers. The right answer depends on dataset size, regulatory constraints, team skills and budget — not on whichever tool we happen to like.

Engagement scope

What a consulting engagement covers

Current-state audit. We map what you actually run today — feeds, scripts, storage, manual steps, who knows what. The audit output is a single diagram with the known failure points circled.

Target-state design. We propose a pipeline architecture matched to your volume and team. For many Australian institutional clients this is modest: a scheduled Python job, a relational warehouse or analytical database, object storage for raw landing, and a thin report layer. It does not need a three-platform data lake.

Build or hand-off. Depending on your team, we either build the first version with you, or produce a specification your IT team can implement. We do not lock you into a proprietary toolchain.

Validation and monitoring. Every pipeline needs tests: row-count checks, value-range checks, source-vs-storage reconciliation, and alerts when a feed does not arrive. We design those in from the start.

Documentation. Runbooks, data dictionaries, on-call notes. When the pipeline breaks at 4pm before an investment committee, the person answering the page should have something to read.

What we will push back on

We will push back on "we need a real-time data platform" when the dataset is refreshed daily and read by four people. We will push back on a multi-cloud architecture when Australian data-residency rules make a single region simpler. We will push back on "let's just load everything into a data lake" when a six-table relational schema would do the job and be auditable. Our job is not to sell you more technology than you need.

Data processing and technical services only. Not financial advice.

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