WHO IS HEIDI?

Heidi is on a mission to halve the time it takes to deliver world-class care.
We believe that by 2050, every clinician will practice with AI systems that free them from administrative burdens and increase the quality and accessibility of care to patients across the world.
Built for clinicians, by clinicians, at the core of Heidi is its people. We are an eclectic bunch of inventors, builders, scientists, nurses, doctors, mathematicians, designers, creatives, and high-agency executors.
We achieve in 6 months what it takes our competitors 4 years to do. In just 12 months, 20 million patient consults were supported by Heidi, and we’re now powering more than 1 million consults every week.
With our most recent $16.6MM round of funding from leading VC firms, we’re geared up to supercharge our ambitious global growth, starting with the US, Canada, UK and Europe - and we need great people like you to get there.

REQUIRED QUALIFICATIONS

• A minimum of 5+ years of experience in data engineering or a related field.
• Proven track record of building and scaling production data systems in cloud environments (preferably AWS and Databricks).
• Expertise in designing and implementing robust data pipelines, ETL processes, and data warehouses.
• Demonstrable experience providing direct data engineering support to Machine Learning teams, including data preparation, feature engineering, and model deployment.

THE ROLE

As a Staff Data Engineer at Heidi Health, you will be a key technical leader responsible for the design, implementation, and evolution of our data infrastructure and architecture. You will play a critical role in building reliable, scalable, and secure data systems that power our core products and AI functions.

WHAT YOU’LL DO:

• Lead the design and implementation of a scalable, secure, and highly available data lakehouse architecture on AWS to support our product and AI teams
• Architect and build robust, production-grade ELT pipelines leveraging technologies such as S3, Athena, Lake Formation, Trino, and Databricks (preferred)
• Own orchestration strategy using tools like Airflow, Databricks Workflows, or Dagster to manage complex, dependency-driven workflows
• Work closely with Machine Learning Engineers to deliver data platforms optimised for model training, deployment, and monitoring
• Drive technical decisions on data tooling, architecture, and engineering best practices—balancing innovation with long-term maintainability and cost-effectiveness
• Embed data privacy, security, and compliance controls aligned with healthcare and industry standards such as HIPAA, SOC 2, and ISO 27001
• Provide technical leadership and mentorship to engineers across the organisation, raising the bar for excellence in data engineering and cross-functional delivery
• Promote a culture of documentation, testing, observability, and operational rigour across all data systems