Data Engineering
Data Engineering & Pipeline Development in Brisbane
Your dashboards are only as good as the data underneath them. We build the pipelines, warehouses, and lakehouses that make your analytics reliable, fast, and trustworthy.
Why data engineering matters
There's a simple diagnostic for whether your analytics are built on solid foundations: if your data team spends more than 20% of their time cleaning and wrangling data each week, your analytics are built on sand.
Every report that requires manual exports, every dashboard that needs "a quick fix" before presenting to the board, every time someone says "that number doesn't look right" — these are symptoms of a data engineering problem, not a BI problem. You can't build a trustworthy Power BI dashboard on top of a fragile, poorly-documented data layer. And you can't build effective AI automation on top of inconsistent, unvalidated data.
Good data engineering is invisible. When it's done right, the data just works — it arrives on time, matches what the business expects, and can be trusted without double-checking. Our job is to build that invisible layer so your analysts and decision-makers can focus on the questions, not the plumbing.
The foundation starts with a clear data strategy — understanding which data needs to flow where, and why. From there, engineering makes it real.
What we build
Every engagement is different, but these are the most common engineering solutions we deliver for Brisbane businesses.
ETL/ELT Pipelines
Reliable, scheduled, and monitored. Data flows from source to warehouse without manual intervention. We build pipelines that alert you when something breaks — not pipelines that silently fail and serve stale data for three days before anyone notices.
Data Warehouse & Lakehouse Architecture
Designed for analytics from day one — not retrofitted from an operational database. We design the schema, partition strategy, and storage layer to match your query patterns and growth trajectory, not a generic template from a blog post.
dbt Data Modelling
A clean, tested, documented transformation layer that your analysts and BI tools work from. dbt gives you version-controlled SQL, automated data quality tests, and a data lineage graph — so you can see exactly where every number comes from. One reliable model, not a dozen competing definitions of "revenue".
Data Quality Monitoring
Automated checks that alert you when source data changes unexpectedly — before it breaks your dashboards. Row count anomalies, schema changes, null rates, referential integrity failures. The monitoring catches problems at ingestion, not when a CFO spots a number that doesn't add up.
ERP & CRM Integration
SAP, Microsoft Dynamics, Salesforce, MYOB, Xero — we've connected them all. Every source system has its quirks: undocumented fields, inconsistent date handling, APIs with pagination edge cases. We've already hit those walls and know how to work around them.
Streaming Pipelines
Real-time data ingestion for operations that can't wait for a nightly batch. IoT sensors, transaction streams, live operational events — processed and available for analytics within seconds. Built on Azure Event Hubs and Apache Spark Structured Streaming.
Our technology stack
We work primarily in the Microsoft Azure ecosystem, with deep expertise in the open-source tools that underpin modern data engineering. Everything we build is documented, version-controlled, and handed over properly.
How an engagement works
Every data engineering project starts with a Discovery Sprint — typically two weeks. We map your current data landscape: every source system, every manual process, every place where data is being extracted and joined by someone in a spreadsheet. We document what exists, assess what needs to change, and produce a prioritised pipeline build plan.
From there, the main build is fixed-scope and milestone-based. You see working pipelines at each checkpoint — not a six-month black box followed by a go-live prayer. Each milestone includes automated tests and documented lineage so you know exactly what was built and why.
After delivery, we offer an optional retainer for ongoing operations and expansion. As your business grows and adds new source systems, we can extend the pipelines without rebuilding from scratch. If you prefer to hand off to an internal team, we provide comprehensive documentation and a handover session — not just a repository link and good luck.
Who needs data engineering
Data engineering work isn't always obvious from the outside. Here are the situations where businesses typically realise they have a foundational problem.
BI tools returning wrong numbers
Your Power BI dashboards exist but nobody trusts them — revenue in the dashboard doesn't match the figure from the finance team. This is almost always a data modelling or pipeline problem, not a visualisation problem.
Integrating two or more source systems
A new ERP, a second CRM, an acquisition that brought its own systems. Getting data from multiple sources into a single reliable view requires engineering — not just a Power Query connection in a spreadsheet. Construction firms with project management, finance, and HR systems in separate platforms know this problem well.
Manual data assembly every week
Someone on your team exports data from three systems every Monday morning, joins them in Excel, and sends a report. This is a pipeline waiting to be built. The manual process is fragile, slow, and tied to one person. When that person is sick, the report is late. When they leave, the knowledge leaves with them.
Fix your data foundation
One conversation is usually enough to identify whether you have a data engineering problem — and what it would take to fix it. No obligation, no sales pitch.