Data Engineer (Data Platform)

We're looking for a Data Engineer to join our Data Platform team at Coinhako.

Every dashboard, fraud signal, financial report and AI-driven analysis in the company depends on data being right. Our team runs the systems that make that true — the metadata repository, the ingestion and ELT pipelines, the warehouse, and the internal tools our teams use every day.

This is a hands-on contributor role on a small team, working inside a platform direction that's already set. You'll own pieces end to end: a pipeline one week, an internal tool the next, a data-quality problem the week after. Some of it is systems that are already live and depended on — those become yours to run and improve. The rest is new.

• Write and operate ingestion and ELT pipelines (Airflow / MWAA) bringing in transactions, product events and third-party feeds
• Take a source we've never used and turn it into a table analysts trust
• Keep pipelines healthy — including ones you didn't write

• Add quality checks that stop bad data reaching reports, and make missing data visible instead of silent
• Reconcile our numbers against independent sources
• Investigate when a number looks wrong, and be able to say why it was — or wasn't

• Add to and maintain the metadata repository that pipelines, apps and analytics rely on
• Document datasets well enough that analysts and AI systems can use them without asking us

• Work on the backend and data layer behind the internal tools our teams depend on — the APIs they call and the metadata that drives them
• Help integrate AI (LLM / RAG, agentic analysis) into the platform and its tools

• Handle requests from analysts and stakeholders, and turn the repetitive ones into something self-service

• 2–4 years in data engineering, backend, or platform engineering
• Strong SQL and Python
• Experience writing and running pipelines with Airflow or something similar
• Familiarity with cloud services (AWS preferred)
• You understand the systems you've built — why they behave the way they do, what the trade-offs were, and what they cost to run
• You've owned something in production end to end — deployed it, looked after it, and fixed it when it broke
• You verify your own work before you call it done
• Comfortable in code you didn't write, on systems that are already live
• Comfortable writing backend code and deploying a service, not only running pipelines
• Clear communication, and the judgement to flag uncertainty early rather than late

• Financial services, fintech or crypto, and handling sensitive transaction data
• Shipping and deploying internal tools or services — APIs, jobs, small apps
• Data-quality tooling — Great Expectations, Soda, dbt tests
• Experience with data you can't get back — streaming retention windows, APIs with no history, anything where missing the read means the observation never existed

• The pipelines you look after run reliably — and when they don't, you know before anyone else does
• Data-quality problems get caught before they reach a report, a dashboard, or an AI system
• New sources go live cleanly, and the next one is easier because of how you did the last one
• Analysts ask fewer repeat questions, because the answer is documented or self-service
• When something breaks, you can explain what happened, what you changed, and why it won't happen the same way twice
• AI tooling is expected, and we provide it. We care that you can explain and defend what you shipped — not that you typed every line

• Friendly and fun start-up work culture
• Convenient work location located in the heart of CBD area
• Generous annual leaves on top of national holidays
• Medical coverage including GP, Specialist, TCM, and more
• Self-care benefits and exciting fitness workshops/webinars
• Vibrant office with a well-stocked pantry

Find out more about Coinhako here [ and don't forget to visit our Careers Page [

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