Role Description This is a Staff-level role in title and in scope, but it's not a role where you'll be directing traffic from a distance. You'll be the only Data Engineer in our ANZ team (for now), which means you're setting the architectural direction and doing the hands-on work. Warehouse infrastructure, ETL and reverse ETL, pipeline builds, fact table design, data dictionary governance, that's all you. The organisational influence is real, but so is the technical execution. The decisions you make here will be ones that engineers, analysts, and clinical teams depend on for a long time. So will the standards you set. This isn't a role where you'll be handed a finished data function to run, you'll be shaping what it becomes, and building a lot of it yourself. What You'll Be Doing: - Defining the architectural patterns, modelling standards, and pipeline conventions that teams across the organisation build on and building them yourself. - Owning the warehouse infrastructure, ETL and reverse ETL processes, authoritative fact tables, and data dictionary governance end-to-end. - Leading the most strategically significant data initiatives in your area and dealing with the blockers, unclear priorities, dependency gaps and quality failures that get in the way. - Working closely with senior stakeholders in product, clinical, and technology to make sure data is part of the conversation early, not bolted on after. - Owning data quality across your domain, auditing gaps, surfacing inconsistencies across markets, and driving the remediation work. - Developing the analysts and junior engineers around you, including the harder conversations about growth and direction. - Helping senior leadership understand what the data function can and can't reliably deliver, so investment decisions are grounded in reality. Qualifications - 10+ years in data engineering, with experience setting technical direction and still being the person who builds it. - Deep hands-on expertise with BigQuery, dbt or equivalent, and AWS. - Comfortable being the most senior data engineering voice in the room while still being deeply in the weeds day-to-day. - A track record of making architectural decisions others have depended on. - Experience working with senior stakeholders and bringing a commercial perspective to data decisions. - Someone who takes developing people as seriously as the technical work. Requirements - Nice to have: Healthcare or regulated industry experience. - Experience scaling a data function through a growth period. - Data observability, data contracts, or governance at scale. Benefits - Work From Anywhere. 🌍 - A competitive salary and awesome benefits package. 💰 - A supportive and positive work environment. 🌟 - Opportunities to grow and develop your career. 📈 - Opportunity to transform lives through alternative medicine. 💡

Role Description Our data function is growing and we're looking for a Senior Data Engineer to take real ownership of the back-end, the warehouse, pipelines, ETL and reverse ETL, fact tables, and the data dictionary that keeps everything consistent. You won't be handed a pristine setup. There are gaps, there's multi-market complexity, and there's real work to do. You'll be the person who designs the architecture that others build on, and you'll have the scope to shape how data engineering is practised here, the standards, the tooling choices, the modelling patterns. If you want to own something meaningful rather than maintain someone else's decisions, this is worth a look. What You'll Be Doing: - Designing and implementing data architecture for new capability areas - modelling approaches in dbt, pipeline reliability decisions, the works. - Owning our ETL and reverse ETL processes, warehouse structure, authoritative fact tables, and data dictionary definitions. - Finding and fixing data quality and coverage gaps across markets, and building the monitoring infrastructure to catch them earlier next time. - Leading data initiatives end-to-end across Product, Analytics, and Clinical teams - managing competing priorities without losing momentum. - Presenting your work and decisions to senior stakeholders, and being comfortable pushing back when the data doesn't support what's being assumed. - Working with our analysts and engineers and gradually lifting the standard of how the team works. Qualifications - 6+ years in Data Engineering with genuine end-to-end ownership, pipelines, warehouse, the lot. - Solid experience with BigQuery, dbt or equivalent, and AWS. - Someone who can deliver under ambiguity and doesn't need a perfect brief to get started. - Good communication, you'll be talking to non-technical stakeholders regularly and need to make it land. Requirements - Healthcare or regulated industry experience. - Reverse ETL tooling or data observability experience. - Experience designing A/B tests or pilots before the fact, not after. Benefits - Work From Anywhere. 🌍 - A competitive salary and awesome benefits package. 💰 - A supportive and positive work environment. 🌟 - Opportunities to grow and develop your career. 📈 - Opportunity to transform lives through alternative medicine. 💡

• Design and build robust, idempotent data pipelines from scratch utilizing a modern data stack. • Design star and snowflake schemas, writing precise, grain-aware SQL to construct scalable data marts. • Write production-grade, unit-tested Python code at the module level, adhering to strong engineering disciplines such as type hinting and testing. • Build and test dbt models across staging, intermediate, and mart layers while managing overall project structure. • Author and deploy jobs using Databricks Asset Bundles (DAB) following documented architectural patterns. • Implement rigorous data quality checks at source, intermediate, and destination layers to prevent silent drops of nulls or duplicates. • Maintain data governance through comprehensive dbt tests and strict documentation-at-merge-time discipline. • Operate securely within a multi-repository architecture, utilizing service principals and ensuring zero personal credentials in production deployments. • Run cross-repository exposure checks prior to merging schema-breaking changes. • Own data pipelines end-to-end, making key technical design decisions and mentoring mid-level engineers through substantive code reviews. • Define overarching technical direction across core data systems, including modeling standards, branching strategies, observability thresholds, and secret management policies. • Act as a technical leader to unblock the team and actively participate in hiring panels to scale the engineering organization.

• Design and build robust, idempotent data pipelines from scratch utilizing a modern data stack. • Design star and snowflake schemas, writing precise, grain-aware SQL to construct scalable data marts. • Write production-grade, unit-tested Python code at the module level, adhering to strong engineering disciplines such as type hinting and testing. • Build and test dbt models across staging, intermediate, and mart layers while managing overall project structure. • Author and deploy jobs using Databricks Asset Bundles (DAB) following documented architectural patterns. • Implement rigorous data quality checks at source, intermediate, and destination layers to prevent silent drops of nulls or duplicates. • Maintain data governance through comprehensive dbt tests and strict documentation-at-merge-time discipline. • Operate securely within a multi-repository architecture, utilizing service principals and ensuring zero personal credentials in production deployments. • Run cross-repository exposure checks prior to merging schema-breaking changes. • Own data pipelines end-to-end, making key technical design decisions and mentoring mid-level engineers through substantive code reviews. • Define overarching technical direction across core data systems, including modeling standards, branching strategies, observability thresholds, and secret management policies. • Act as a technical leader to unblock the team and actively participate in hiring panels to scale the engineering organization.