Junior Data Engineer
Role details
Job location
Tech stack
Job description
They are seeking a Data Engineer / Analytics Engineer to help build and maintain the core experimental data platform that underpins scientific development, validation, and scale-up activities.
This is a hands-on, technically engaging role operating at the intersection of data engineering, analytics, and laboratory experimentation. You will be responsible for transforming raw, real-world experimental and sensor data into structured, trusted datasets used daily by scientists and engineers.
This position is ideally suited to someone who enjoys working with messy, real-world data and wants to contribute directly to the commercialisation of next-generation clean energy technology.
Key Responsibilities
Build and maintain data pipelines for ingesting laboratory and plant data (CSV, Excel, sensor and time-series sources).
Work closely with engineering and scientific teams to understand experimental data flows, formats, and validation needs.
Clean, transform, and structure datasets for analysis, modelling, and reporting.
Support the development of metadata standards to improve experiment traceability and comparability.
Contribute to dashboards and analytical views used by R&D teams.
Monitor data quality and investigate anomalies or pipeline issues.
Improve documentation, data standards, and best practices.
Progressively take ownership of operational data workflows under the mentorship of a senior data engineer.
Requirements
You will ideally bring:
Strong Python skills for data processing and analysis.
Solid SQL capability and experience working with structured datasets.
Experience or interest in time-series data and sensor-driven environments.
A proactive, self-starting mindset with strong problem-solving ability.
Comfort working within a fast-moving, technically complex start-up environment.
A collaborative approach to working with scientists and engineers.
Desirable:
Exposure to cloud platforms (GCP or AWS).
Familiarity with analytics tools and dashboards.
Experience with laboratory, scientific, or industrial datasets.
Interest in hydrogen, energy systems, or electrochemistry.
Understanding of ETL pipelines and data modelling concepts.
Benefits & conditions
Package & Benefits
Share options available.
Flexible start and finish times.
Flexible hybrid and remote working.
Support for international remote working.
Pension: 3% employee / 5% employer.
Team milestone celebrations.
Referral bonuses.
Strong exposure to a wide range of technologies and accelerated personal development
