Data Engineer
Resumo da vaga pela JobGrid
Data Engineer at Habitat Energy: Austin, Estados Unidos; Híbrido; Tempo inteiro; TI; Engenharia de dados. JobGrid adds normalized role facts, source context, and a path to the employer application page so candidates can compare the listing before applying.
- Location and workplace: Austin, Estados Unidos, Híbrido
- Role classification: TI, Engenharia de dados, Tempo inteiro
- Source freshness: checked by JobGrid on 2026-05-27.
- Application path: candidates continue to the employer application page with non-personal referral tags.
Data Engineer
Habitat Energy is a fast growing technology company focussed on the physical and financial optimisation of energy storage and renewable generation assets globally through complex models and trading. By maximising the returns from these assets we aim to drive investment in renewable energy and accelerate the transition to a low carbon world. Our rapidly growing team of 130+ people in Austin, TX, Oxford, UK, and Melbourne, Australia brings together exceptionally talented and passionate people in the domains of energy trading, data science, software engineering and renewable energy management.
We have a vacancy for a Data Engineer to join our Austin based team.
Your responsibilities will include:
- Supporting Data Engineering Infrastructure:
- Contribute to the design, development, implementation and continuous improvement of our data engineering tools, workflows, processes, and platforms. This includes enhancing the architectural foundations and integrating new data management technologies.
- Writing Well-Structured Code:
- Develop clean, maintainable, well-documented code that adheres to best practices. Support best coding practices within Habitat's software, machine-learning, and data science teams.
- Enhance data engineering knowledge:
- Improve expertise within the software team and ensure their ability to support and collaborate on the data infrastructure infrastructure.
- Data Quality Management:
- Continuously enhance data quality across multiple dimensions such as accuracy, availability, performance, and accessibility to ensure a clear understanding of data within the company.
- Providing backup/escalation to the tech-on-call team.
- Communicating effectively across Software and Data Science teams.