
Vortexa is a fast-growing international technology business founded to solve the immense information gap that exists in the energy industry. By using massive amounts of new satellite data and pioneering work in artificial intelligence, Vortexa creates an unprecedented view on the global seaborne energy flows in real-time, bringing transparency and efficiency to the energy markets and society as a whole.
The Role:
Vortexa is seeking a proactive and driven Account Manager to join our growing commercial team in Houston. This is a foundational role in our Account Management function, working closely with global colleagues to shape how we engage and grow client relationships.
In this role, you will own expansion for a portfolio of clients to include both GRR and NRR, acting as the primary commercial contact. You’ll collaborate closely with Sales, Customer Success Managers and Solution Architects to deliver the best-in-class experience and drive long-term account growth. This is a hands-on role where you’ll engage directly with clients, build trusted relationships, identify and close new opportunities to maximise value.
You must be a self-starter that thrives in the face of challenge, with a resourceful and positive approach to problem-solving in a fast-paced and constantly evolving scale-up environment.
You will be responsible for:
Requirements
It would be great if you also:
Benefits

Building the future of energy markets
Vortexa tracks more than $3 trillion of waterborne energy trades per year in real-time, providing energy and shipping companies with the most complete picture of global energy flows available in the world today. Vortexa’s highly intuitive web-based app and programmatic API/SDK interfaces help traders, analysts and charterers make high-value trading decisions with confidence, when it matters the most.
The web-based platform shares highly detailed oil & gas products flows, produced by hard data, machine learning and state-of-the-art technology with oversight from in-house global industry experts providing real-world context to continually train and improve the models.