About the role
<p><strong>Visium combines strategy, frontier AI, and deep regulated-industry expertise to build and deploy AI-native systems that reinvent how businesses operate - unlocking the value of AI at scale.</strong></p><p></p><p>We're not a strategy firm that ships slide decks and walks away. Not a tech consultancy that builds what won't get adopted. Not a product company chasing problems it doesn't understand. We're all three at once: strategy, deep technical and integration expertise, and product, working as one. That combination is our flywheel. Consulting keeps us close to the hardest real-world problems. Products let us solve them at scale. Every engagement sharpens our products; every deployment makes the next engagement faster, deeper, and more certain.</p><p></p><p>We're obsessed with outcomes. While 95% of enterprise AI pilots never reach production, Visium is built to be the 5% delivering productised AI that compounds in value, not point solutions that deliver marginal gains. We are AI-native, not AI-adjacent. We don't bolt AI onto broken workflows; we redesign them with AI at the core.</p><p></p><p><strong>Role</strong></p><p>As a <strong>Lead Data Engineer</strong>, you will play a key role in designing, building, and scaling modern data platforms for our clients. </p><p>You will:</p><ul><li>Assess and understand clients’ <strong>data landscapes</strong>, including sources, architectures, and data quality </li><li> Collaborate with <strong>business and IT stakeholders</strong> to translate requirements into robust data solutions </li><li> Design, build, and maintain <strong>scalable data platforms</strong> (pipelines, data lakes, data warehouses) on modern cloud infrastructures </li><li> Implement <strong>data models, schemas, and governance frameworks</strong> to support analytics and reporting needs </li><li> Contribute to <strong>technical sales and pre-sales</strong> activities, including solution design and estimations </li><li> Work with <strong>large-scale, distributed data systems</strong>, troubleshoot issues, and stay current with data engineering best practices</li></ul>