Senior Data Engineer

We’re partnering with a mission-driven HealthTech company to find a talented Senior Data Engineer .

In this role, you'll work on healthcare data pipelines and platforms, with a strong focus on building robust ETL processes and scalable data architectures. You'll collaborate closely with cross-functional teams to deliver high-quality, reliable data solutions that support impactful health outcomes. You will develop and maintain our data infrastructure, working with both data processing technologies and storage systems. Your commitment to best practices will be essential as you build secure data pipelines, implement efficient ETL processes following healthcare standards, optimise data workflows for performance, and ensure our data solutions are highly scalable and maintainable.

You'll tackle diverse technical challenges across our data stack, emphasizing security-first development and systematic approaches to data architecture. From designing efficient data processing pipelines to implementing robust data governance, this role requires meticulous attention to established processes and standards. You'll focus on writing clean, maintainable code while implementing robust security measures for sensitive healthcare data and following our comprehensive development guidelines.

We believe in fostering a collaborative engineering culture where knowledge sharing and process improvement are fundamental. You will actively participate in security reviews, code reviews, technical discussions, and contribute to our documentation efforts. Through structured pair programming sessions, technical workshops, and regular team knowledge sharing, you'll help establish and maintain best practices for healthcare data management while expanding your technical expertise. Your role will be crucial in building scalable, secure data solutions that enhance our ability to deliver quality healthcare services.

Key Responsibilities

Design and implement ETL pipelines for healthcare data using Python, SQL, and cloud-based tools

Develop and maintain data infrastructure on Azure including data lakes, warehouses, and processing frameworks

Create robust data transformation processes ensuring data quality, consistency, and security

Implement orchestration to automate manual workflows such that they are reliable, repeatable, secure and robust

Collaborate with Data Science and Clinical teams to understand data requirements and deliver actionable insights

Apply security-first practices in all data processing solutions to protect sensitive patient information

Contribute to team knowledge through documentation and sharing of data engineering best practices

Help maintain compliance with ISO27001 and ISO13485 requirements for healthcare data management

Participate in architectural planning to improve overall data infrastructure and capabilities

Required Experience

Strong experience with Python, SQL, and ETL processes for healthcare data pipelines

Proven expertise managing data in Azure, such as Azure Data Factory, Azure Databricks, or Azure Synapse Analytics

Experience with data lake and data warehouse architectures

Experience building and maintaining data platforms that enable data scientists to work effectively in Azure Machine Learning environments

Strong understanding of healthcare data standards (HL7, FHIR, OMOP, SNOMED) and data modelling

Knowledge of data governance, data quality, and master data management practices

Experience implementing monitoring and logging for data pipelines

Ability to optimise data workflows for performance and scalability

Understanding of security best practices for sensitive healthcare data

Good communication skills and ability to collaborate with wide ranging stakeholders

Enthusiasm for solving complex data engineering challenges

Desired Skills

Experience in startup environments

Experience working in a healthcare technology company

Familiarity with healthcare technology and regulatory requirements (ISO13485, ISO27001)

If you are interested in this position please apply via the below link.