DATA ENGINEERING THAT DELIVERS
Does your data work for you — or are you working around it?
Every Power BI dashboard, every AI model, every business decision powered by data has one thing in common: it’s only as good as the data engineering underneath it.
Without solid data engineering, businesses end up with reports that don’t match, numbers that need manual fixes every week, and analytics teams spending more time cleaning data than analysing it. Hiring a full-time data engineer is one solution — but for most Australian businesses, it’s an expensive and slow one.
On Report provides data engineering services as part of how we build reporting and analytics solutions. We design and build the data infrastructure that makes your dashboards reliable, your AI agents trustworthy and your business data actually useful.
Deliverables
What's included in our data engineering service
Strategy
- Data source discovery and audit
- Current state assessment — structure, quality, gaps
- Architecture design and fixed-price proposal
Build
- Data pipeline design and development
- ETL / ELT transformations and data modelling
- Microsoft Fabric, Azure Data Factory or dbt builds
- Automated refresh and orchestration
Support
- Pipeline monitoring and maintenance
- Schema and source change management
- Retainer-based data engineering support available
Handoff
- Documentation of pipelines, models and data flows
- Team walkthrough and knowledge transfer
- Access, permissions and governance configuration
How we work
A data engineering process built for business outcomes
Clear milestones, honest communication and no surprises. Here’s how we take you from messy, siloed data to a data engineering foundation you can build on.
Discovery & Requirements
We start by understanding your business data landscape — what systems you run, where data lives, what questions you need to answer and what’s broken about how you get there today. We audit every relevant data source and document the current state honestly, including data quality issues, gaps and dependencies.
Strategy & Scoping
We come back with a clear architecture design and a fixed-price proposal. You’ll see exactly what we’ll build — the pipelines, the transformation logic, the data models and the end state — before work begins. No scope creep, no surprises.
Build + Delivery
We develop your data pipelines and models using the right tool for your environment — Microsoft Fabric, Azure Data Factory, dbt, or a combination. We test every transformation against real data, configure automated refresh schedules and deliver a solution that runs reliably without manual intervention.
Training & Ongoing Support
We document everything we’ve built, walk your team through the architecture and make sure someone in your business understands how it works. Ongoing support retainers are available — whether that’s a data engineer on call for changes, or a managed service for teams without internal capability.
DATA ENGINEERING EXPERTISE
What our data engineers build
Data Pipelines
We design and build automated pipelines that extract data from your source systems, transform it into the shape your reports and models need, and load it into the right destination — reliably, on schedule, every time.
Data Modelling
We build semantic data models that define the relationships between your business entities — customers, products, transactions, time — so every report and AI query draws from a consistent, governed single source of truth.
ETL / ELT Development
Whether your architecture calls for transforming data before loading (ETL) or after (ELT), we design and implement the right approach for your volume, latency requirements and toolset.
Microsoft Fabric
We build data engineering solutions natively in Microsoft Fabric — lakehouses, data flows, pipelines and semantic models — so your data engineering, analytics and AI capabilities sit in one unified, governed environment.
Data Quality & Governance
We build data quality checks, validation rules and monitoring into every pipeline we deliver. Bad data in means bad decisions out — we make sure the data your business runs on is accurate, complete and trustworthy.
Data Warehouse & Lakehouse Builds
We design and implement data warehouses and lakehouses that centralise your business data in a structured, query-ready format — whether on Azure, Microsoft Fabric, Snowflake or another cloud data platform.
FAQ
Common questions about data engineering
- Still have questions?
We’re happy to help. Reach out to discuss your needs, challenges, and how AI can fit your business.
What is data engineering?
Data engineering is the discipline of designing, building and maintaining the infrastructure that moves, transforms and stores data. A data engineer builds the pipelines and models that sit between your source systems — your CRM, ERP, accounting software, databases — and the dashboards, reports and AI tools your business uses to make decisions. Without data engineering, reporting is manual, inconsistent and slow.
What does a data engineer do?
A data engineer designs and builds the systems that make data usable at scale. This includes building data pipelines that extract data from source systems and load it into a centralised data platform; writing transformation logic that cleans, reshapes and enriches data; designing data models that define how business entities relate to each other; and maintaining the infrastructure so it runs reliably and scales as your data volume grows.
Do I need to hire a data engineer, or can On Report help?
For most Australian businesses, hiring a full-time data engineer is expensive, slow and often more capability than you need right now. On Report provides data engineering as a service — we deliver the infrastructure you need as a scoped, fixed-price project, with ongoing support available as a retainer. You get the output of a data engineer without the overhead of a full-time hire.
What data engineering tools and platforms do you use?
We work primarily within the Microsoft ecosystem — Microsoft Fabric, Azure Data Factory, Azure Data Lake Storage, Azure Synapse and Power BI. We also work with dbt, Snowflake, Databricks, PostgreSQL, SQL Server and a range of API and file-based integrations. The right tool depends on your existing environment, data volumes and reporting requirements — we design the architecture around what’s right for you, not what we happen to prefer.
How does data engineering relate to Power BI?
Power BI is a reporting layer — it visualises data that’s been prepared and modelled. Data engineering is what happens before Power BI. A well-built data pipeline ensures the data Power BI connects to is clean, current and correctly structured. Without it, reports break, numbers disagree and your analytics team spends more time fixing data than analysing it. We deliver both — the engineering foundation and the reporting layer on top of it.
My data is messy. Can you still work with it?
Yes. Most of our data engineering engagements start with data that’s inconsistent, duplicated, poorly documented or spread across multiple systems that don’t talk to each other. Data quality assessment is a standard part of our discovery process — we’ll tell you exactly what you’re working with, what it means for the project and how we’ll address it.
How long does a data engineering project take?
Smaller projects with a focused scope — one or two data sources feeding a single reporting environment — can be delivered in four to six weeks. Larger implementations involving multiple systems, complex transformation logic or a full data warehouse build may take three to six months. We scope every engagement individually and provide a timeline as part of the proposal.
- Still have questions?
We’re happy to help. Reach out to discuss your needs, challenges, and how AI can fit your business.