Data Engineering Services Australia
The data foundation your analytics and AI systems depend on
Great dashboards and machine learning models are only as good as the data feeding them. Voxotec builds the pipelines, platforms, and infrastructure that keep Australian businesses running on reliable, fresh, well-structured data.
99.9%
Pipeline uptime SLA
10M+
Events processed daily
<5min
Avg. data freshness
40%
Avg. infra cost reduction
Services
Data engineering capabilities
Data Pipeline Development
Batch and real-time pipelines that ingest, transform, and load data from any source to any destination, reliably, with full monitoring and alerting.
Cloud Data Platform Setup
End-to-end setup of modern data platforms on AWS, GCP, or Azure, including data lake, warehouse, compute, and security configuration.
Data Modelling & dbt
Clean, documented, tested data models using dbt. Consistent metric definitions and transformation logic that your analysts and ML engineers can trust.
Real-Time Streaming
Event-driven architectures using Kafka, Kinesis, or Pub/Sub. Stream processing for fraud detection, live dashboards, and operational intelligence.
Data Quality & Observability
Automated data quality tests, anomaly detection on pipelines, and alerting so your team knows about problems before your business does.
Platform Migration
Move from on-premise databases, legacy ETL tools, or expensive proprietary platforms to modern, cost-efficient cloud infrastructure, with zero data loss.
Why it matters
Bad data infrastructure kills good analytics
You can invest heavily in beautiful dashboards, advanced ML models, and talented analysts, but if the underlying data is stale, incomplete, or untrusted, none of it delivers value. We have seen organisations spend months building analytics capabilities on top of broken pipelines.
Data engineering is the unglamorous foundation of every successful data organisation. It is the work that makes everything else work. Done well, it is invisible, data just arrives, clean and fresh, exactly when and where it is needed.
Done poorly, it means your data team spends the majority of their time debugging pipelines, reconciling broken joins, and explaining why the numbers changed, instead of generating insights.
We build data infrastructure that is reliable, observable, and maintainable. We use modern open-source tooling and cloud-native services that your team can operate without vendor dependency. And we document everything, so the next engineer on your team can understand the system in hours, not months.
Technology
Our data engineering stack
Warehouses
Snowflake, BigQuery, Redshift, Databricks
Orchestration
Apache Airflow, Prefect, dbt Cloud
Streaming
Apache Kafka, AWS Kinesis, Google Pub/Sub
Transformation
dbt, Spark, Pandas, SQL
Cloud
AWS, GCP, Azure
Storage
S3, GCS, Azure Data Lake, Delta Lake
Related
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Is your data infrastructure holding you back?
Tell us about your current stack and your biggest data pain points. We will identify the highest-leverage changes and give you an honest estimate of what is involved.