Data platforms that cost less and break less.
We fix slow, expensive and unreliable data platforms, and build new ones properly the first time. Part of our fee is tied to results.
Hands-on with the platforms you already pay for
- Snowflake
- BigQuery
- Databricks
- Redshift
- dbt
- Airflow
- Fivetran
- Kafka
- Spark
- AWS
- Google Cloud
- Azure
- Postgres
- Looker
- Power BI
Start small. Pay for outcomes.
Every engagement starts with a fixed-price Health Check, so you know what's wrong and what fixing it is worth before you commit to more.
Are you paying too much for data?
Answer four plain-English questions about your data bill. See a likely savings range and what to ask your team. No technical knowledge needed, and you see your estimate instantly.
Check my data bill →Sound familiar?
Most of the companies we talk to have one of these problems, and often all three.
The cloud bill keeps climbing
Snowflake, BigQuery or Redshift costs grow faster than the business, and no one can say exactly why.
Check what you could save →Pipelines break every week
Dashboards show yesterday's numbers, or the wrong ones. Your engineers spend their week firefighting instead of building.
No one trusts the numbers
Finance, product and ops each have their own version of revenue, so every meeting starts with an argument about the data.
What this looks like in practice
Recent work Heryanto led or helped design. Clients are not named.
A whole bank's data platform for about US$10k a month
Designed and built a listed digital bank's entire data platform from scratch. It now serves the whole bank for around US$10k a month, about 0.2% of revenue.
Sensitive data, safely in the cloud
Helped design the security layer that lets a major Australian bank analyse sensitive customer data in the cloud. Without it, only a minority of the bank's use cases could move to cloud and capture the cost savings.
Led by Heryanto Lee
More than 13 years building and running data platforms for banks, fintech and SaaS companies. Heryanto was Group Head of Data and AI at a listed digital bank, Head of Data and AI at a SaaS company, and Data Engineering Manager at a Southeast Asian super-app. He now consults on data for a major Australian bank.
Four steps, no long retainers
Free call
30 minutes on your stack, costs and pain points. If we're not the right fit, we'll say so.
Health Check
Two weeks, fixed price. A clear diagnosis and a costed plan you can act on with or without us.
Fix or build
Rescue or Foundations, with targets agreed up front and part of our fee tied to hitting them.
Hand over
Documentation, training and runbooks so your team owns it. Ongoing leadership is optional.
Frequently asked questions
What does a data engineering consultant do?
A data engineering consultant designs, builds and repairs the systems that move and store your data: pipelines, warehouses, data models and the tooling around them. At Nadi we focus on two things: getting a first proper data platform in place, and fixing platforms that have become slow, unreliable or expensive.
How much does a data engineering consultant cost in Australia?
Senior contract data engineers in Australia typically charge A$750–1,400 a day. Our fixed-price Data Health Check starts at A$8,000. Larger engagements combine a base fee with a success fee tied to agreed outcomes, so part of our fee depends on results.
Do you only work with Melbourne companies?
No. We're based in Melbourne and work with companies across Australia, including Sydney, Brisbane, Adelaide and Perth. Most work is remote, with on-site workshops when they help.
Which tools and cloud platforms do you work with?
We work with the common modern data stack: BigQuery, Snowflake, Redshift, Postgres, dbt, Airflow, Fivetran and Airbyte, on Google Cloud, AWS or Azure. We recommend tools based on your size and budget, not on partnerships.
Can you reduce our Snowflake or BigQuery bill?
Usually, yes. Most overspend comes from architecture rather than individual queries: full reloads that should be incremental, oversized warehouses, unused tables and duplicated pipelines. Our Data Rescue engagement targets these, and our success fee is a share of verified savings.
Is our data ready for AI?
Usually not yet, and that's normal. AI tools need data that is complete, consistent, documented, fresh and properly access-controlled. Our Health Check includes an AI-readiness assessment that scores your data on each of these and ranks your AI ideas by how feasible they are today.
What is pay-for-performance pricing?
You pay a base fee that covers our costs, plus a success fee only if we hit outcomes agreed up front, such as a cost reduction, pipeline reliability or delivery milestones. The success fee is capped, so there are no surprises.
Not sure where to start?
Book a free 30-minute call. We'll tell you honestly whether you need us, and what we'd look at first.