Senior Analytics Engineer — AI-Driven Healthcare Data

Heidi; Melbourne; On-site; AUD 120,000 - 180,000; Full time

Benefits offered by this job

Learning & development budget

Health and wellness allowance

Home office budget

Parental leave 26 weeks (primary)

Parental leave 18 weeks (secondary)

Fertility support up to $10,000

Work from anywhere 4 weeks/year

Equity

Job summary

Heidi is building the future of healthcare with an AI care partner that powers clinical analytics and health system insights. The Analytics Engineer will partner with analysts and AI engineers to design scalable data models, build robust pipelines on AWS/Azure, and ship data products like the Heidi API.

You’ll transform complex data into trusted insights, ensure PHI-safe data handling, and enable self-serve dashboards for diverse teams.

Qualifications

• 3–5 years of experience in analytics engineering, data engineering or a similar data-focused role in a fast-paced or high-growth environment.
• Strong proficiency in SQL and experience transforming data for analysis, reporting and product use cases.
• Working knowledge of Python and PySpark.
• Experience with modern data platforms and workflows, ideally including dbt, Databricks, Airflow, Snowplow, and reverse ETL or BI tools such as Omni.
• A solid understanding of data modelling, data quality and testing best practices.
• Ability to work with analysts, engineers, AI teams and business stakeholders to translate data needs into scalable, reliable solutions.
• Interest in AI and LLM workloads, such as evaluations or model-based enrichment, and an appetite to go deeper alongside AI engineers.
• Experience supporting or developing customer-facing data products, and care in handling sensitive health data such as PHI and PII.
• Strong communication skills and a focus on clarity, documentation and long-term maintainability.
• Curiosity, accountability and a drive to improve how data is built, managed and used across the business.

Responsibilities

• Partner with analysts, data engineers and business teams to design, build and maintain scalable data models that power reliable insights, experimentation and performance tracking.
• Work alongside Clinical AI, LLMOps and Applied AI engineers to bring evaluations and LLM-powered data enrichment into production.
• Develop and manage data pipelines across our regional workspaces on AWS and Azure, with accuracy, consistency and trust built in.
• Use strong SQL and analytical thinking to turn complex data into clear, usable structures for analysis and reporting.
• Design product event tracking with engineers so new features ship with data the business can trust.
• Contribute to customer-facing data products, such as the Heidi analytical API and data shares with health systems, and make sure data is accurate, accessible and PHI-safe.
• Improve access to data so teams can self-serve insights, from Omni dashboards to reverse ETL into tools like Braze and HubSpot.
• Champion data quality, testing, documentation and governance across all analytics workflows.
• Contribute to shared practices, frameworks and AI-assisted tooling that make analytics more efficient, consistent and scalable.

Skills

SQL

Python

PySpark

dbt

Databricks

Airflow

Reverse ETL

Omni

BI tools

Data modeling

Data quality

Tools

dbt

Databricks

Airflow

Snowplow

Omni

Reverse ETL

Job description

Heidi is building the future of healthcare with an AI care partner that powers clinical analytics and health system insights. The Analytics Engineer will partner with analysts and AI engineers to design scalable data models, build robust pipelines on AWS/Azure, and ship data products like the Heidi API.

You’ll transform complex data into trusted insights, ensure PHI-safe data handling, and enable self-serve dashboards for diverse teams.