Flower optimizes and trades flexible energy assets such as wind, solar, batteries and EV chargers from Stockholm. Its stack pairs industrial protocols (MQTT, Modbus, AWS IoT Core) with a heavy data layer (Databricks, Spark, Snowflake, dbt, Dagster), so asset telemetry feeds trading and forecasting models directly. Senior-weighted, accelerating hiring targets multi-market trading scale and large-scale BESS development in Germany.
Flower is a Stockholm-based energy company founded in 2020 that uses machine learning to optimize and trade renewable and flexible energy assets. Its portfolio covers wind and solar farms, battery storage systems and EV chargers, and it works to make renewable output more predictable on the grid. The company has 51–200 employees and is privately held. Hiring is concentrated in Sweden across engineering, sales, data, energy, product and security functions.
Flower runs Python, Go and FastAPI on AWS (ECS, EKS, IoT Core) with Terraform, a data layer of Databricks, Spark, Snowflake, BigQuery, dbt, Airflow and Dagster, plus MQTT and Modbus for asset connectivity.
Yes. Hiring is accelerating and engineering is the largest focus, mostly at senior level, alongside sales, data, energy, product and security roles, all based in Sweden.
Flower is building short-term forecasting models for energy prices and volumes, an asset integration platform, edge device provisioning, and large-scale battery storage development, including expansion in Germany.
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Flower's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.
