Data Engineer for Healthcare AI & Analytics Platform

Accurx; Greater London; On-site; GBP 85,000 - 105,000; Full time

Benefits offered by this job

£85,000 - £105,000 base salary

Equity: £30,000 in equal share options

Office-first culture in Shoreditch, 3+

Job summary

Accurx is hiring a Data Engineer to design and scale a robust data architecture that handles massive event-driven flows across GP practices in the UK. You will own ingestion and ELT pipelines, using Python and SQL to feed a Snowflake data warehouse.

You will partner with Analytics Engineering, Product and ML teams to turn raw healthcare data into high-quality datasets, uphold data integrity and security, and contribute to AI/ML foundations for production models.

Qualifications

• Extensive experience as a Data Engineer designing and building ingestion and ETL/ELT pipelines in production using Python and SQL.
• Proficient with modern cloud data platforms (Snowflake, BigQuery, or Redshift) and data transformation tools like dbt.
• Comfortable owning data architecture decisions and working with Git and CI/CD practices.
• Experience or strong interest in operationalising ML models and leveraging AI tools.

Responsibilities

• Design and scale robust data architecture for event-driven flows across UK GP practices.
• Build and own end-to-end ingestion and ELT pipelines into a data warehouse.
• Collaborate with Analytics Engineering, Product and ML teams to create reliable datasets.
• Set standards for data integrity, security and reliability in healthcare data.

Skills

Data engineering

Python

SQL

Snowflake

BigQuery

Redshift

dbt

Git

CI/CD

Machine Learning

AI tools

Tools

dbt

Git

CI/CD

Snowflake

BigQuery

Redshift

Job description

Accurx is hiring a Data Engineer to design and scale a robust data architecture that handles massive event-driven flows across GP practices in the UK. You will own ingestion and ELT pipelines, using Python and SQL to feed a Snowflake data warehouse.

You will partner with Analytics Engineering, Product and ML teams to turn raw healthcare data into high-quality datasets, uphold data integrity and security, and contribute to AI/ML foundations for production models.