="Hello, " & "world"

Ivon Chin

Cloud EngineerBI & Data EngineeringContact Centre TechCertified INDEX/MATCH enjoyer

20+ years of work, from the shop floor and the phones to data platforms and the cloud. I turn messy business needs into systems people actually decide with.

SELECT * FROM projects

Note · B2

This cell contains 20+ years of experience. Handle with care.

#BOO!

not an error.

That's just the domain.

Calculating… (4 threads)Count: 9 employersSum: 0 yearsAverage: delightful

Career, as a Gantt chart

Chart 1 · Career!A1:C5

Change Chart Type

Chart 1fx=SERIES("Duration",Career!$A$2:$A$5,Career!$D$2:$D$5,2)

Career timeline

Early career

Retail, service, small business & media

iSelect

Reporting Analyst → WFM Technical Lead

Bubs Australia

BI, Data & Systems Lead

TTEC VoiceFoundry

Cloud & Data Engineer

20032008201220182022now

Selected work

work_query.sql

work_query.sqlconnected: ivon.career

1SELECT project, company, stack, vibe
2FROM   ivon.projects
3WHERE  built_from_scratch = TRUE
4ORDER BY impact DESC;  -- no NOLOCK was harmed

	company	stack	vibe
▸	Bubs Australia	Domo, Aurora, SQL	built from zero
2	TTEC VoiceFoundry	Connect, Glue, Kinesis	cloud-native
3	TTEC VoiceFoundry	Lex, Lambda	bots with manners
4	Bubs Australia	Excel, Power Query, DAX	dairy maths
5	Bubs Australia	IoT, Aurora, Domo	sensors everywhere
6	Bubs Australia	Domo, Excel	spreadsheet → system
7	iSelect	Aspect Via, Power BI	migrated, not panicked
8	TTEC VoiceFoundry	Bedrock, Athena, Python	25,000 calls, $5
9	TTEC VoiceFoundry	Athena, Redshift, QuickSight	governed at scale
10	TTEC VoiceFoundry	Kinesis, Grafana, Lambda	live, not overnight
11	TTEC VoiceFoundry	Connect, CloudWatch, boto3	30+ customers, calm
12	Self-employed	Python, vision LLMs	294 messy forms
13	Personal	Node.js, MCP, TypeScript	agents on agents
14	Self-employed	Next.js, React, Supabase	8 sites shipped

(14 rows returned) · Query completed successfully, and so did the projects. Click a row for the full case study.

SELECT * FROM ivon.projects WHERE id = 1;1 of 14

Bubs Australia

Enterprise BI, ETL & Data Platform

challenge: A fast-growing FMCG company needed data infrastructure that finance, operations, supply chain and the executive team could all decide from.
solution: Architected and built the whole platform from scratch. Integrated MYOB, Xero and Unleashed, supported the SAP rollout, and built the warehouse, the ETL pipelines and the reporting for every business function.
impact: One version of the truth, company-wide. The board saw its KPIs, finance got forecasting and cost models, and operations got real-time inventory and logistics.

techDomoAWS AuroraMYOBXeroUnleashedSAPPower QueryETLSQL

What sets me apart

Strengths!A1:C13

=INDEX(Strengths!A1:C13, MATCH(, Strengths!A1:A13, 0), MATCH("strength", Strengths!A1:C1, 0))

Result · Strengths!B3

Data, Reporting & ETL Architecture

Owns the whole data journey, from source extraction to the dashboard.

ProofBuilt Bubs Australia's data warehouse and ETL from scratch, giving finance, ops and the board one version of the truth.

All 12 strengths, with the problem each one solves
	A	B	C
1	problem	strength	in one line
2	Our shiny new system is ignored by the floor		Builds for how work actually gets done, not how the diagram says it does.
3	Our reports don't match each other		Owns the whole data journey, from source extraction to the dashboard.
4	We run the business on a giant spreadsheet		Enterprise-grade models with the accessibility of a spreadsheet.
5	We need forecasts we can actually trust		Cost models, forecasts and reporting that speak the language of finance.
6	Our contact centre needs to move to the cloud		From Aspect Via to Amazon Connect: the tech and the KPIs behind it.
7	Our data is stuck in five different systems		Migrations, serverless and integrations that hold up in production.
8	Things fall apart after go-live		Requirements, build, support, iterate. Doesn’t vanish after go-live.
9	Finance and IT don't speak the same language		Turns stakeholder needs into specs, and specs back into plain English.
10	We want AI that doesn't annoy customers		Chatbots and voice agents that fit the customer journey.
11	AI sounds great until we see the token bill		Turns unaffordable AI workloads into coffee-money jobs by designing around token economics.
12	Production is down and nobody knows why		Finds root causes in distributed cloud systems while the customer watches the clock.
13	We need one person who can cover a lot of ground		Happiest where one person has to bridge several disciplines.