Data Engineer

The opportunity

As a Data Engineer at myTomorrows, you will play a key role in shaping the data infrastructure that powers our product platform and supports data-driven decision-making across the company. Working closely with our Data Analysts, DevOps team, and Product & Engineering colleagues, you will build a robust and scalable data environment that enables reliable data flow, storage, and visualisation.

What you’ll do in this role

• Work within the Engineering and Product teams, closely collaborating with Marketing and Growth to develop reliable reporting pipelines
• Design, build, and maintain robust data and reporting pipelines that deliver actionable insights to business stakeholders
• Develop and evolve scalable, reliable data architectures for analytics and reporting use cases
• Partner with business analysts and stakeholders to understand reporting needs and translate them into effective data solutions
• Ensure compliance with industry standards and best practices for healthcare data management and data quality
• Continuously improve data reliability, observability, and documentation
• Stay up to date with emerging trends and technologies in data engineering and analytics

What you bring to the table

• Strong experience building and maintaining modern data pipelines using Python, DBT, and ETL tools such as Airflow
• Hands-on experience loading and transforming data from multiple sources, including HubSpot, Google Analytics, relational databases (MySQL, PostgreSQL), NoSQL databases (e.g. DynamoDB), and various data formats
• Experience working with data visualization tools such as Tableau
• Solid understanding of data modeling, data architecture, and analytics best practices
• Working knowledge of software development lifecycles and agile methodologies
• Strong problem-solving skills and the ability to collaborate effectively within a small, cross-functional data team
• Clear and confident communication skills in English

What success looks like in 6 months

After 6 months, the Data Engineer has developed a deep understanding of our data stack, domains, and business context, and is operating as a trusted technical partner across the organisation. They have audited and improved the existing data architecture, proactively identifying and addressing data quality gaps through automated validation and alerting.

They independently design and own end-to-end data models, translating ambiguous business requirements into scalable, maintainable technical solutions. Stakeholders rely on them to shape data roadmaps, align data strategy with business goals, and make sound architectural decisions.

The data platform is more reliable, performant, and cost-efficient as a result of their work, with pipelines meeting high standards for stability and efficiency. Beyond execution, they have led at least one cross-functional initiative that raises the overall engineering bar and contributes to defining the next generation of our data platform architecture.

Current tech stack

We are fully cloud-native, leveraging AWS and adopting a Lean, API-first product development approach driven by modern cloud technologies and thoughtful design practices.

Our backend applications are built using Python, FastAPI, Pydantic, and SQLAlchemy (MySQL). On the front end, we predominantly develop with Angular and also utilize Headless Wagtail and WordPress for select products. To ensure code quality and a consistent release pipeline, we employ GitHub Actions for CI/CD along with tools like Renovate, ruff, Poetry, and pre-commit.

We containerize our applications using Docker and Docker Compose. Currently, we are transitioning from AWS Lambda-based deployments to Kubernetes-managed services and moving from AWS CloudFormation to Terraform for infrastructure as code. We continue to expand our capabilities in knowledge graphs (Neo4j) and database technologies (MySQL, DynamoDB).