The first SaaS environmental accounting platform for the food industry, CarbonMaps' mission is to help the sector reduce its environmental footprint by collecting and analyzing data at every stage of the food chain, from farmers to consumers.
CarbonMaps' SaaS platform enables food industry players to carry out rapid, high-precision assessments of the environmental impact of their products, ingredients and agricultural raw materials.
CarbonMaps is based on widely recognized scientific models and environmental standards (GHG Protocol, ISO 14040 & 14044, IPCC).
Mission highlight Embrace the opportunity to pioneer our dataengineering team as we revolutionize sustainability in the agro industry As one of our first hires, you'll be at the forefront of building our data infrastructure, but your role will extend far beyond that.
We're a lean team, hungry for innovation, and that means wearing multiple hats and exploring a multitude of missions.
Responsibilities Designing, building, and maintaining data pipelines for the lifecycle analysis of a diverse range of food products (multi factor analyses:
carbon, water, land use, biodiversity etc) Conducting data exploration, cleaning, and validation to ensure data integrity and reliability in collaboration with climate & methodology scientists Leading exploratory research to enhance our semantic algorithms (LLMs), constantly pushing the boundaries of what's possible in environmental analysis.
Optimizing the performance and scalability of pipelines to process large volumes of data in real time or in batch mode Developing the mid-term technical vision and roadmap and anticipate future requirements and infrastructure needs Guiding and mentoring a junior data scientist Profile and competencies Advanced degree in Computer Science, Engineering, or a related field.
Minimum of 4 years of hands-on experience in data/software engineering, with a track record of successfully architecting and optimizing data pipelines in real-world environments.
Proficiency in Python, SQL, and relevant dataengineering frameworks (Kafka, Pub/Sub) Familiarity with machine learning concepts and frameworks is advantageous, but not mandatory; however, a keen interest in exploring and integrating ML techniques into dataengineering workflows is essential.
Prior experience in mentoring or managing junior team members is desirable, but not required; a passion for knowledge sharing and fostering a culture of growth is essential.
