Dreamware / Services / Data & Analytics / Data Engineering & Pipelines
Data Engineering & Pipelines
ETL/ELT, streaming, data lakes, warehousing.
About this service
Data engineering is the unglamorous foundation of everything data-related. Before you can analyse data, before you can train models, before you can build dashboards — the data needs to be collected, cleaned, transformed, and stored in a form that's useful. We build that foundation.
We design and implement data pipelines using modern ELT patterns: extract data from source systems, load it to a central store, then transform it there using the compute power of the warehouse. We work with dbt for transformation logic, giving you version-controlled, tested, documented transformation code rather than scattered SQL scripts.
For organisations with real-time requirements, we build streaming data pipelines using Kafka or cloud-native equivalents. For organisations with large, varied data assets, we design data lake architectures using S3 or Azure Data Lake that balance storage cost with query performance.
How Dreamware approaches this
We start by understanding your data sources, your analytical requirements, and your existing infrastructure. Data engineering decisions are heavily influenced by data volume, update frequency, and latency requirements — a pipeline that works for weekly batch reporting is very different from one that needs to support real-time dashboards.
We build pipelines with quality in mind: data validation at ingestion, schema evolution handling, monitoring and alerting for pipeline failures, and lineage tracking so you know where your data came from. We use infrastructure-as-code for pipeline infrastructure and treat transformation code with the same engineering standards as application code.
What you get
- Data pipeline — production-grade ETL/ELT pipelines with monitoring and alerting
- Data warehouse or data lake — structured storage layer appropriate to your requirements
- Transformation layer — dbt models with tests, documentation, and lineage
- Data quality framework — validation rules and monitoring for data quality metrics
- Pipeline documentation — data dictionary, lineage diagrams, and operational runbook
- Orchestration setup — Airflow, Prefect, or cloud-native orchestration for pipeline scheduling
Investment guide
Data engineering projects typically run $20,000–$80,000 NZD. Simple pipelines from a handful of sources into a warehouse run $20,000–$35,000. Complex multi-source pipelines with streaming requirements, data quality frameworks, and full orchestration sit higher. Data engineering retainers are available for organisations wanting ongoing pipeline development and maintenance.
All pricing in NZD excluding GST. Fixed-price engagements where scope allows — we'll confirm pricing after a free scoping conversation.
Ready to get started?
Book a free conversation. We'll tell you honestly what's realistic, what it costs, and how we'd approach it.