Data engineering in Melbourne
Universities in Melbourne report on students constantly: to their councils, to faculties planning next year's intake, and to admissions teams watching applications week by week. The data comes from a student management system, a learning platform, a CRM for admissions and a finance system, and the reports that combine them are often rebuilt each cycle by analysts who know exactly which extract to trust.
We build pipelines that land each source once, with an agreed definition of a student, an enrolment and a completion, and checks that fail loudly when a feed changes shape. Every figure in a report traces back to the system it came from. On FM360 we run hourly syncs into ClickHouse and learned to normalise every value at the boundary before comparing anything, which is exactly where university data usually disagrees with itself.
Melbourne's software market is built on products rather than projects. SEEK, REA Group and MYOB run from the city, Culture Amp started here, and a generation of engineers learned their trade inside marketplaces and SaaS businesses that sell well beyond Australia. The SaaS companies we hear from have usually found their market and are now paying for early decisions: a data model that assumed one customer type, permissions that started as a role column, and infrastructure in a single region when their newest customers are in the US or Europe.
Health and education are the other two pillars. The Melbourne Biomedical Precinct around Parkville brings together hospitals, research institutes, biotech firms and universities, and they need registries, trial platforms, patient apps and operational tools that handle sensitive records carefully and still suit a busy clinician between patients. The universities (Melbourne, Monash, RMIT and the rest) run student systems, learning platforms and research data at a scale that rarely fits off-the-shelf software, and their students judge every portal against the apps on their phones. And the Port of Melbourne keeps a large freight and logistics industry busy, much of it still run on email and spreadsheets.
Hiring costs a little less than in Sydney. Indeed puts the average base salary for a software engineer in Melbourne at A$109,570, from 329 reported salaries, and the engineers who have already scaled a product are the ones every scale-up wants at the same time. A platform problem can sit on the roadmap for quarters while that hire is found. That is where we fit: senior engineers on a defined piece of work within days, with the code in your repositories from the first commit.
Our four hours of daily overlap land in your afternoon, and we move them to suit your team. Your morning stays with your own people, and the afternoon is when you talk to the engineer writing your code, in stand-ups, reviews and design decisions. Runbooks and architecture notes are written as we go, so the work stays readable long after the engagement ends.
We are the right fit for a platform problem your product team has no time for, a defined piece of work with a clear end, or the months before your own hires start. Senior engineers arrive within days, the first piece is a fixed-price two weeks, and agencies can put the work under their own name.
Four ways this arrives.
Two definitions of the same metric, computed in two places. We pick one, write it as a tested model in version control, and point every report at it, so the argument moves from whose number is right to what the definition should be.
A job reports success while the report still shows yesterday. We give every table a written freshness guarantee, monitor what the reader actually sees rather than the job that feeds it, and alert a person when the guarantee is missed.
A CRM, an ERP or a work-order tool reachable only through its API. We build the sync with retries, rate limits and normalisation at the boundary, and keep append-only snapshots so last Tuesday is a query rather than a restore.
Analytical queries running against the application database at the worst possible moment. We move them to a columnar store, keep Postgres for transactions, and feed one from the other by change data capture or scheduled extracts, whichever the freshness requirement calls for.
A nightly work-order extract into append-only ClickHouse snapshots, with a verifier that reads the live Tableau dashboard to prove it shows the newest data.
Read the write-up →Asked by Melbourne teams.
How do you work with teams in Melbourne?+
We are in Bengaluru, and four hours of every working day overlap with your afternoon, moved to suit your team. Stand-ups, code reviews and design calls happen live in that window, so your mornings stay with your own people. We work in your tools, whether that is Slack, GitHub or Linear, and the person on every call is the engineer who writes the code.
Do you build for Melbourne SaaS and marketplace companies?+
Yes, and it is the work Melbourne asks for most. We untangle data models that assumed one customer type, turn a role column into proper permissions, add single sign-on and a second region for the first large overseas customer, and tune search and messaging for marketplaces that have outgrown their first version. We work inside your codebase, alongside your product engineers, starting with whatever is slowing them down most.
How does your rate compare to hiring in Melbourne?+
Indeed puts the average base salary for a software engineer in Melbourne at around A$110,000. Our published rate is $35/hour with a $5,000 minimum, or $5,400 a month for one embedded engineer. There is no recruiting time and no employment overhead, and work can start within days of the call. The first piece is a fixed-price two-week engagement at $2,800, so you can judge us on what ships.
Can you work white-label for a Melbourne agency?+
Yes. If your agency wins more build work than your developers can carry, we work under your name. Your client sees your team and your process, we join calls as your engineers, and the code goes into your client's repositories from the first commit. You keep the client relationship. An NDA is signed before you share anything specific, and the runbooks and architecture notes are written as we go.
Do we need a data warehouse yet?+
Maybe not. If your reporting runs comfortably against a Postgres read replica, a warehouse adds a second system to pay for and keep in step. The signs you need one: analytical queries slowing the product, data from several sources that has to be joined, or history you must keep that the application overwrites. We will tell you which side of that line you are on.
Why ClickHouse rather than Snowflake or BigQuery?+
Not always. For steady, high-volume analytical work, ClickHouse is fast and cheap to run, self-hosted or on its managed cloud, and we run it in production. Snowflake and BigQuery ask less of you operationally and suit ad hoc querying across a large team. If nobody on your side wants to operate a database, we will recommend the managed option even when the invoice is larger.
What does a freshness guarantee actually mean?+
A written statement per table or report, such as "never more than an hour behind" or "reflects yesterday's close by 07:00". Each one has a check that measures it and an alert that fires when it is missed. Without it, stale data looks exactly like correct data until somebody makes a decision on it.
Can you work with the tools we already have?+
Usually, yes. If you already run dbt, Airflow, Fivetran or a BI tool your team knows, we build inside it. We suggest replacing a tool only when it is the cause of the problem you hired us for, and we show you the evidence before asking you to fund a migration.
How do you handle personal data in the warehouse?+
We decide what should reach the warehouse at all before building the pipeline. Identifiers can be hashed or tokenised at ingestion, sensitive columns restricted by role, and retention enforced by the pipeline itself. Every field is documented from source to dashboard, so anyone can see where a number came from and who can read it.