Hydrosat is a space and data analytics company building a new Earth Observation constellation and software to unlock the power of thermal infrared imagery. Our mission is to capture and deliver high spatio-temporal resolution thermal infrared data, currently not available from other sources, to provide insights that benefit the environment, enhance food and water security, and serve other critical applications. Our downstream applications team in Luxembourg combines best-in-class remote sensing, machine learning, and cutting-edge fusion approaches to thermal imagery to deliver innovative and scalable solutions to the agri-food industry.
We are looking for bright, creative, and ambitious people who can work in a dynamic and collaborative start-up environment. Hydrosat is determined to change the way people use space imagery and revolutionize agriculture. Join us on this mission and help transform the world.
What you’ll do
As a Data Engineering Manager, you will lead and mentor a team of data engineers developing and delivering high-quality software solutions. You will work closely with cross-functional teams, including product and program management, and translate business goals into reliable, scalable systems. Your role will involve hands-on technical guidance, development, code reviews, and supporting the growth of your team.
Responsibilities
- Drive technical innovation and improvements
- Ensure the team’s work quality is high and adheres to engineering standards
- Review architecture and design decisions
- Define team and individual objectives and key results
- Coach and mentor team members
- Define team processes and best practices
- Provide support with hands-on coding, help the team with critical tasks, and code reviews
- Facilitate communication within the team and with other stakeholders
- Foster a positive team culture
- Hire, onboard and retain engineering talent
- Conduct performance reviews and provide feedback
Requirements
- 10+ years of data engineering experience, with a solid understanding of data engineering principles and practices
- 2+ years in a leadership role, with proven experience in building and coaching high-performing data engineering teams, including defining team objectives and aligning them with organizational goals.
- Proficiency in designing scalable, efficient, and maintainable data pipelines and architectures.
- Experience in building and managing ETL/ELT workflows for data extraction, transformation, and loading.
- Experience working with cloud services and infrastructure
- Demonstrable experience building effective working relationships with engineering teams
- Strong team player with demonstrated ability to take ownership and drive execution
- Excellent organizational and communication skills
- Proficiency in English (written and spoken)
Desired Qualifications
- Master’s degree in engineering
- Experience with orchestration frameworks to automate and monitor workflows, such as Apache Airflow or Dagster.
- Basic understanding of MLOps for deploying machine learning models within data pipelines.
- Understanding of MLOps best practices for monitoring, versioning, and maintaining models in production.
- DevOps experience (Docker, Kubernetes)
- Experience in Earth observation, AI, geospatial, imaging science, or computer vision fields
Benefits
- Attractive compensation
- Stock option plan
- Fast paced and dynamic start up environment
