carbmee is seeking a Sustainability Data Engineer to design and build ETL pipelines that transform ERP data (with SAP emphasis), PLM/product data, procurement data, and flat files into carbmee's data model. This is an infrastructure-and-integration role with a strong sustainability domain component, including deciding how customer data maps into our schema, handling missing fields, and scaling pipelines from pilot deployments to enterprise deployments. Responsibilities include designing and maintaining data pipelines for ingestion, data validation and deduplication, handling missing values, and encoding policy-driven decisions that avoid incorrect numbers; developing integrations to customer systems via APIs, Python/SQL scripts, and batch/streaming workflows; working with LLM-assisted mapping tools to accelerate transformations while implementing guardrails against mis-mapping; collaborating with customer IT and data teams to understand architectures and remove blockers; feeding recurring data-quality patterns back to product and engineering teams; and contributing to internal tooling and templates so future engineers can onboard customers faster. Your profile: candidates typically have 1-2 years of data engineering, backend development, or technical consulting experience, strong Python and SQL fundamentals, and genuine interest in the sustainability domain. Senior candidates should have a track record of building and scaling production data pipelines (ERP integrations such as SAP are a strong plus) and be comfortable owning architecture decisions with minimal oversight. Fa

carbmee is seeking a Sustainability Data Engineer to design and build ETL pipelines that transform ERP data (with SAP emphasis), PLM/product data, procurement data, and flat files into carbmee's data model. This is an infrastructure-and-integration role with a strong sustainability domain component, including deciding how customer data maps into our schema, handling missing fields, and scaling pipelines from pilot deployments to enterprise deployments. Responsibilities include designing and maintaining data pipelines for ingestion, data validation and deduplication, handling missing values, and encoding policy-driven decisions that avoid incorrect numbers; developing integrations to customer systems via APIs, Python/SQL scripts, and batch/streaming workflows; working with LLM-assisted mapping tools to accelerate transformations while implementing guardrails against mis-mapping; collaborating with customer IT and data teams to understand architectures and remove blockers; feeding recurring data-quality patterns back to product and engineering teams; and contributing to internal tooling and templates so future engineers can onboard customers faster. Your profile: candidates typically have 1-2 years of data engineering, backend development, or technical consulting experience, strong Python and SQL fundamentals, and genuine interest in the sustainability domain. Senior candidates should have a track record of building and scaling production data pipelines (ERP integrations such as SAP are a strong plus) and be comfortable owning architecture decisions with minimal oversight. Fa