Data Implementation Engineer

We're accesso Intelligence (powered by Dexibit). 🦖 🎢 🐅 🗼 🖼️ 🎡 🏟️

accesso Intelligence is the intelligence layer for the business of fun — serving iconic visitor attractions around the world. Not just data, but AI that understands how attractions actually work, helping teams make insightful decisions then act on them: getting more visitors through the door, spending, engaging, leaving happily and returning loyally, with efficiency.

With 25 years of deep industry experience behind us and Dexibit's AI pedigree inside us, you get the best of both worlds: startup culture close to the ground up, with the scale of a listed company behind you.

accesso Intelligence sits on top of the world's richest cross-vendor dataset for visitor attractions — integrating with 100+ industry source systems across ticketing, F&B, retail, queuing, CRM, operations and more. That data foundation is what makes everything else possible: the conversational insight, the forecasting, the benchmarking, the AI.

We're looking for a Data Implementation Engineer to own the technical delivery of accesso Intelligence integrations, then executing that pattern reliably and at volume across our customer base. You’ll be building upon, and maintaining, our class-leading data platform.

What makes this role distinct is the combination of technical execution with genuine understanding of how attractions actually operate commercially. A revenue share arrangement between a venue and an operating partner, a multi-operator ticketing setup, a seasonal membership model with complex redemption logic — these aren't edge cases, they're the norm. Getting the data right means understanding the business behind it.

Location: Auckland based, hybrid (~3 days in our Parnell office / optionally ~2 at home)

Reports to: VP, accesso Intelligence

Travel ✈️ Requirement: None

• Establish the integration patterns. Work with the data engineering team to define how accesso Intelligence connects to each accesso product. Data model, transformation logic, semantic mapping, quality thresholds. This is foundational work you'll do once per product (and refine over time) rather than reinvent per customer.
• Scale and maintain across customers. Onboard new customers onto each integration — configuring per venue, validating data integrity against our acceptance criteria. Then own the ongoing health: change requests, configuration updates, and diagnosis when something breaks or drifts.
• Build derived datasets and transformations. Beyond raw integration, you'll help build the derived datasets, transformations and semantic mapping that turn source data into meaningful, analytics-ready inputs — the layer between what a system outputs and what accesso Intelligence needs to deliver insight.
• Close the feedback loop into accesso. You'll be the eyes and ears of integration reality. We all want to find the global optima. Sometimes that means feedback into the product and engineering teams to shine a light on opportunities and future use cases.
• Work directly with customers on data. You'll engage with customer technical and operational contacts through onboarding and beyond — handling data queries, managing expectations on timelines and data quality, and translating between what customers have and what the platform needs.

• You're fluent in Python and SQL, comfortable with data platforms and transformation tools, and have experience building and maintaining integrations with APIs and third-party systems. Our stack runs on DuckDB and MotherDuck — you don't need prior experience with them, but you should be the kind of person who finds that interesting rather than a detour from the tools you'd rather be using.
• You know data doesn't exist in a vacuum. You build a working mental model of how operators actually run: their business rules, revenue arrangements, data quirks and organisational structure. Not just their schema.
• You're pattern-minded. You find the repeatable approach, document it, and execute at volume — without letting consistency become rigidity when a customer's situation genuinely requires something different.
• You're customer-oriented without losing your technical edge. You can sit on a call with a customer's data team, translate their needs into integration requirements, and communicate clearly about what's possible and when.
• You care about data quality like it's personal. Acceptance criteria, integrity checks, derived logic — you hold the line on what good looks like, because you know the difference between a quick fix and the right one.

There's no single path to this role, especially in the age of AI. You might be:

• A data or software engineer who's done integration or implementation work and wants a role where you can see the direct impact of your work on real customer outcomes

• Someone from a SaaS implementation or solutions engineering background who has strong data fundamentals and wants to go deeper technically

• A data analyst or BI engineer who's crossed over into pipeline and integration work and thrives at the intersection of technical delivery and customer context

• Someone from the attractions, hospitality or cultural sector who's worked closely with data systems and wants to turn operational knowledge into implementation expertise