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Transthoracic Echocardiography (TTE) is the most widely-used screening method for the detection of pulmonary hypertension (PH), a life-threatening cardiopulmonary disorder that requires accurate and timely detection for effective management. Automated PH risk detection from TTE can flag subtle indicators of PH that might be easily missed, thereby decreasing variability between operators and enhancing the positive predictive value of the screening test. Previous algorithms for assessing PH risk still rely on pre-identified, single TTE views which might ignore useful information contained in other recordings. Additionally, these methods focus on discerning PH from healthy controls, limiting their utility as a tool to differentiate PH from conditions that mimic its cardiovascular or respiratory presentation. To address these issues, we propose EchoFM, an architecture that combines self-supervised learning (SSL) and a transformer model for view-independent detection of PH from TTE. EchoFM 1) incorporates a powerful encoder for feature extraction from frames, 2) overcomes the need for explicit TTE view classification by merging features from all available views, 3) uses a transformer to attend to frames of interest without discarding others, and 4) is trained on a realistic clinical dataset which includes mimicking conditions as controls. Extensive experimentation demonstrates that EchoFM significantly improves PH risk detection over state-of-the-art Convolutional Neural Networks (CNNs).