Ha Hoang Hao

Data & Research Analyst · Indie Hacker

I analyze data by day and build tools for people who work with it on the side.

About me

I'm a Data & Research Analyst and Indie Hacker based in Ho Chi Minh City, Vietnam.

I've spent 4 years in market research and analytics — owning pipelines, cleaning messy datasets, delivering analysis, turning noisy data into decisions. Somewhere along the way I became obsessed with a different question: what would it look like if working with data was actually enjoyable?

That's what I build toward. survy automates the parts of survey data work nobody wants to do by hand - querizer makes learning SQL genuinely fun.

Still employed. Still building.

My Projects

querizer
A SaaS I built around one idea: SQL practice should actually be enjoyable.
AISQLPythonTypeScriptNext.jsFastAPIDockerShadcn
survy
Open source Python library for automated survey data processing, transformation and analysis with a clean, scriptable API. Shipping with AI integration extension - enable LLM-powered data analyzing workflows using the agent skills pattern.
PythonPolarsAISPSSPyPIGithub Actions

My Story

Insight Asia - Quantitative Research Executive

2024 — Present

This is where I learned what survey data really looks like at scale — and how painful it is to process without the right tools. I owned the data side of the pipeline: from questionnaire programming through to cleaning, transforming, analysis, and dashboards. Every project had its own schema, its own quirks, its own manual steps that nobody had ever bothered to fix. That friction is what eventually became survy — a library I built to automate the parts of the workflow I found myself repeating across every single project.

Deli - Data & Research Analyst

2022 — 2024

My first job out of university. I spent two years close to the business — tracking sales performance, mapping trends across regions and product lines, and gathering ground-level feedback from sales reps, distributors, and end users to inform product decisions. It taught me how real data looks: messy, inconsistent, and nothing like textbook examples. That's also where I learned that the most valuable thing you can do with data isn't the analysis — it's removing the manual work standing between raw data and insight.