We are looking for a self-driven Data Systems Engineer to help build and improve the data systems that support our products and research workflows.

This is a hands-on technical role spanning production Python, data pipelines, databases and internal tooling. You will work on the libraries and infrastructure that underpin our data products, improve how data is stored and processed, and help make our systems more reliable, scalable and easier for others to work with.

You should be comfortable taking ownership of well-defined technical problems, working independently through implementation, and collaborating with data scientists, researchers and engineers to turn requirements into practical solutions.

Roles and Responsibilities

· Design, build and maintain production data pipelines and internal Python libraries used across data and research workflows.

· Support migration and modernisation of existing data pipelines and systems.

· Contribute to data modelling, transformations and downstream data marts.

· Improve the reliability of data systems through validation, testing, monitoring, logging and alerting.

· Build reusable tooling that makes common data and research workflows simpler and more reliable.

· Investigate and resolve data, application and pipeline issues across production systems.

· Contribute to deployment automation, environment management, CI/CD and infrastructure-as-code.

· Collaborate with data scientists, researchers and engineers to translate requirements into scalable technical solutions.

· Maintain strong engineering standards through testing, documentation, code review and clear technical communication.

· Use AI tooling effectively while understanding, reviewing and being able to defend the code you ship.

Required Qualifications

· 2-4+ years of professional experience in software engineering, data engineering or a related technical role.

· Experience with PySpark, Apache Iceberg, dbt or modern lakehouse technologies.

· Experience building internal Python packages, libraries or CLI tools.

· Experience with AWS, GCP or Azure.

· Strong Python skills, with experience writing clean, reusable and maintainable production code beyond standalone scripts.

· Strong SQL skills and hands-on experience working with relational databases such as PostgreSQL, MySQL or similar.

· Experience building or supporting data pipelines and working with structured datasets.

· Good understanding of data modelling, data quality, failure handling and operational reliability.

· Experience working with production systems in a cloud or deployed environment.

· Strong debugging and problem-solving skills, with the ability to work independently and take ownership of technical tasks.

· Experience working within an engineering team through code reviews, documentation, tickets and technical communication.

· Comfortable collaborating with data scientists or researchers and working with analytical or data-intensive workflows.

Desirable Skills

· Experience with Docker, CI/CD pipelines and Terraform or other infrastructure-as-code tooling.

· Experience with Grafana or similar monitoring and observability platforms.

· Experience with workflow orchestration tools such as Airflow, Prefect or Dagster.

· Experience optimising systems or queries for large datasets.

· Familiarity with security best practices for application code, databases and data workflows.