We’re not looking for someone who builds from scratch on a clean system with unlimited time and budget.
We’re looking for someone who has inherited a broken, multi-source financial data environment and rebuilt it into something the business actually trusts. Then wired it up for AI. 🚀
“That’s not a moon. That’s a data platform with no governance, no reconciliation, and six different definitions of revenue.”
The Role
You will own the architecture, build, and ongoing integrity of our data and AI platform. This means designing for financial accuracy today and AI-readiness tomorrow. You are hands-on. You write the models, own the pipelines, and sit in the room when the CFO asks why the numbers don’t match.
This is not a strategy role. This is not a documentation role. You build, you own, you deliver.
The business is at an inflection point. We have real transaction volume, real compliance obligations, and real ambitions to embed AI into how we make decisions. What we need now is someone who can make the data underneath all of that bulletproof, and then build the infrastructure that lets AI actually do something useful with it. 🤖
Financial Data Integrity 🔒
You will design and maintain a single source of truth in Snowflake. You will build and manage CDC pipelines that handle high transaction volumes cleanly and without data loss. You will implement snapshot and locking frameworks so month-end numbers stop shifting after financial close. You will solve identity resolution across multiple CRMs, payment systems, and trading platforms, building deterministic mapping logic that the business can rely on across jurisdictions.
You will build reconciliation frameworks that give the business genuine confidence in its numbers, closing the gap between what the source systems say and what the warehouse reports. Trust comes from reconciliation, not from dashboards that look nice.
AI and Machine Learning Infrastructure 🧠
On the AI side, you will architect the data foundations that make AI actually work. This means designing AI-ready data models from the ground up, building and maintaining feature stores for real-time ML consumption, and integrating LLM pipelines and RAG architectures into the platform in a way that is auditable and production-grade.
You will work closely with data scientists and product teams to ensure the data feeding into models is clean, consistent, and governed. You understand that a model is only as trustworthy as the data it was trained on, and that financial AI without data integrity is a liability, not an asset.
You will also help define how AI outputs are monitored, logged, and reconciled back to source data so the business can explain and defend what its models are doing. In a regulated environment, that matters enormously.
Platform Ownership 🛠️
Beyond building, you will own. That means quarterly data reviews with business stakeholders, ongoing data quality monitoring and alerting, schema evolution that doesn’t break downstream consumers, and a clear architectural roadmap that the business can invest behind with confidence.
What You’ve Done Before
You have rebuilt a messy financial data environment inside a fintech, payments company, neo-bank, prop trading firm, or similarly high-transaction business. You didn’t just design it on a whiteboard. You got your hands dirty, fixed the pipelines, wrote the dbt models, backfilled the history without corrupting the reporting, and got the CFO to trust the numbers again.
You have hands-on depth across Snowflake, dbt, Airflow, and CDC pipelines. You understand idempotent transformations, late-arriving data, and replay logic. You have implemented Type 2 slowly changing dimensions, point-in-time reconstruction, and immutable reporting tables. You know what month-end locking actually means in practice and you have built systems that enforce it.
On the AI side, you have worked with data scientists closely enough to understand what they actually need from the data platform. You have thought seriously about feature engineering, model serving infrastructure, and what it takes to make LLM outputs trustworthy in a production financial environment.
“Never tell me the odds” is not an engineering principle. You know your numbers, your pipelines, and exactly where the risk is. ⚡
You have worked in environments where data errors cost real money, triggered compliance incidents, or caused reporting restatements. You take data quality personally, because you have seen what happens when it fails.
The AI Piece 🤖
This is not an afterthought and it is not a future roadmap item. We are actively building a platform that will underpin AI-driven decision making across the business and we need someone who understands both sides of that equation.
You will shape how we approach LLM integration, how we build and serve features for ML models, and how we ensure AI outputs can be trusted, audited, and explained to a regulator if necessary.
You will have strong opinions about what makes a feature store actually useful versus theoretically interesting, and you will have the scars to back those opinions up.
If you have designed data platforms with downstream AI and ML consumption baked into the architecture from day one, you will be at home here. If you have only bolted AI onto an existing platform as an afterthought, we will know.
Must Haves ✅
Snowflake, dbt, Airflow, CDC pipelines, and PowerBI. High-transaction financial environment experience. Strong understanding of AI and ML data requirements including feature engineering, model serving infrastructure, and LLM-ready pipeline design. A track record of delivering reconciled, auditable, trustworthy financial data. Someone who builds and owns, not just advises and moves on.
Nice to Haves 👍
CFD trading, payments, or regulated environment exposure. Multi-currency and multi-entity data modelling. RAG pipeline design and LLM integration experience. Familiarity with ASIC, FCA, or MAS regulated environments. Experience designing data platforms that feed both operational AI and compliance reporting simultaneously.
What You Won’t Find Here 🚫
Someone who produces architectural documents but doesn’t write code. Someone who wants to start from zero with a greenfield build and a blank canvas. Someone who has only ever worked in SaaS marketing analytics and thinks that counts as financial data experience. A consultant who delivers and disappears.
If that’s you, this role is not for you. If it isn’t, keep reading.
4 to 5 days a week on-site in our clients plush CBD offices
The Package 💰
Up to $215,000 base salary plus superannuation plus a milestone bonus tied directly to platform delivery. This is a role for someone who wants to be rewarded for what they actually ship.
This role is being placed exclusively by Big Wave Digital 🐳, specialists in elite Tech and Digital Marketing recruitment.
Talk to Keiran or Jules, two of Australia’s most connected recruiters in the technology space, with a track record of building high-performance teams for ambitious fintech and digital businesses. They don’t just fill roles. They help companies build the teams that define what comes next.
Big Wave Digital. Helping you build your perfect team. 🏄
Reach out directly to Keiran or Jules to find out more or to put your hand up for this one.
May the data be with you. Always. ✨