Nithin Prasad
Analytics Lead · Melbourne, Australia
Data is a trust problem.
I solve it end to end.
Analytics Lead in Melbourne. Six years taking ambiguous business problems — from a CEO's whiteboard to a governed data model to a dashboard people actually open on Monday morning. Now building AI-native tools that make traditional BI look like a fax machine.
Nithin at a glance
10+
Years exp
20+
Clients
200+
SQL models
Delivery impact
How I work
“From CEO whiteboard to Monday morning dashboard.”
Selected Work
Things I've built
Speech-to-Dashboard: AI Report Generation
Built an internal tool that lets my team narrate requirements to AI and receive polished, interactive HTML dashboards — far beyond anything Tableau or Power BI can produce. The output is live in production across 10+ enterprise clients.
Hours → minutes to build a dashboard
10+ enterprise clients on the output
Replaced Tableau licences entirely
Allied Health Data Platform — 8 Brands, One Truth
Built a unified semantic data model across an 8-brand NDIS allied health group — consistent business rules and definitions, row-level security, compliance flags, and dashboards used from CEO to individual clinician. Also automated tiered bonus calculations that replaced a month-end spreadsheet nightmare.
Pharmacy Payments Platform — Built Twice, Better the Second Time
Built a month-end payment calculation system for a pharmacy delivery company, then rebuilt it as a full customer data platform when Xero changed their API limits and broke the original solution. The second version gave both pharmacies and the business real-time visibility on orders and amounts owed — something the first version never had.
Writing
Thinking out loud
SQL: The Skill That Never Expires
Tools come and go. Platforms get replaced. But SQL has been the common language of data for over 50 years and it is not going anywhere. If you want a career in data, master your SQL.
The First Conversation Is Never About Data
When I walk into a new client engagement, I am not thinking about data sources or pipeline architecture. I am trying to figure out three things the client does not know I am assessing.
Why I Stopped Building Dashboards and Started Building Trust
The biggest mistake I see in data work is treating it as a reporting problem. It is not. It is a trust problem. Here is what that distinction actually means in practice.
About
Technical depth. Business context.
Both in the same person.
Started writing code at Microsoft and Cognizant. Moved to Melbourne, pivoted to data at Monash. Six years at Firehawk Analytics — going from writing SQL to owning full client engagements, leading a team of five, and building AI tools that replace entire BI platforms. The role kept changing. So did I.
The real problem
Most companies treat data as a reporting problem. It's actually a trust problem. Beautiful dashboards built on messy definitions get questioned once, investigated for two days, then quietly abandoned.
How I start
The first conversation is never about data. I ask clients to walk me through their process — how decisions actually get made, where gut feel fills the gap, who the real decision maker is (versus who they said it is).
What I've built
There are people who use AI tools. People who build with AI tools. And people who've shipped something real with AI that someone else's business depends on. I'm in the third group.
What I believe
The boring work matters most. Clean models, consistent definitions, automated testing, documentation. Without that foundation, you're just automating bad data faster.
Career Timeline
Senior Analytics Engineer / Analytics Lead
Firehawk AnalyticsCurrent
- 10+ enterprise clients across Snowflake & Databricks — C-suite to delivery
- Built AI report gen tool (OpenAI + Claude): 3× faster, replaces Tableau/Power BI
- 200+ dbt models in production · 40% fewer data quality incidents · 100+ dashboard users
Business Intelligence Analyst
The Salvation Army – Employment Plus
- 15+ Power BI dashboards — 60% reduction in manual reporting
- ETL automation saving 10+ hrs/week, +35% data accuracy
Master of Business Information Systems
Monash University
- Moved to Melbourne — pivoted from software to data
- Monash Top 1% globally
The short version
I spent six years at Firehawk doing every part of this job — sitting with CEOs to define what to measure, building the dbt models that make those metrics trustworthy, delivering the dashboards, and making sure they actually get used. That last part is harder than it sounds.
I also built a Speech-to-Dashboard tool using OpenAI and Claude that my team uses to generate rich, interactive HTML reports from natural language. Clients get something that looks nothing like Tableau or Power BI — because it isn't. The magic is the clean data layer underneath. AI without that is just automating bad data faster.
The role kept evolving: SQL writer, then client lead, then team lead of five, then AI product builder. I'm looking for a bigger environment to bring that same end-to-end mindset to.
Analytics Lead, Head of Data, or Senior Analytics Engineering roles in Melbourne or remote.
Reach out → nithinprasad93@gmail.comTechnologies I work with
dbt
Snowflake
Databricks
Python
Power BI
Tableau
BigQuery
Airflow
OpenAI
GitHub
HubSpot
Salesforce
Microsoft Azure
Xero
Looker
AnthropicContact
Let's build something.
Data platform, AI analytics layer, or just want to compare notes on where analytics is heading — I'm always up for a conversation.