Unity Advisory

Manager

• Play a pivotal role in delivering advanced analytics and strategic insights to help clients make better financial and commercial decisions throughout the deal lifecycle.
• Supporting both buy-side and sell-side transactions and delivering post deal value creation projects through advanced analytics and AI.
• Lead projects challenging the status quo, collaborating closely with clients, advisors and colleagues.
• Translate complex data findings into clear recommendations for senior stakeholders, frequently presenting insights and reports to C-level clients.
• Develop and automate impactful board and management reporting solutions to provide business unit-level insights with market context.

Manager – Deals Analytics

Full-stack Engineer

• Lead the design and evolution of the firm’s core internal AI platform, including shared architectures, pipelines, and frameworks.
• Define target-state architectures spanning applications, data, infrastructure, and integration layers.
• Evaluate and guide the adoption of emerging AI approaches and capabilities where they create real internal value.
• Ensure systems are designed in line with governance, security, and regulatory requirements.
• Work closely with engineering, data, and product stakeholders to deliver robust, production-ready internal systems.
• Guide early-stage initiatives through to scalable, maintainable production implementations.
• Define integration strategies between the AI platform and internal enterprise systems.
• Optimise platform environments for performance, scalability, reliability, and cost efficiency.
• Establish architectural standards, patterns, and best practices across the engineering function.
• Advise senior leadership on platform risks, trade-offs, and long-term architectural decisions.
• Stay current with advances in platform and AI system design, incorporating relevant innovation into the firm’s roadmap.

Engineering Lead

Data Engineer

• Designing, building, and operating scalable data foundations that enable analytics, AI use cases, and data-driven decision-making.
• Developing robust data models and semantic layers using Snowflake and AWS RDS.
• Implementing and maintaining modern data stacks across warehousing, ingestion, and orchestration tools.
• Optimizing data tracking and collection frameworks.
• Supporting governance and compliance requirements, including GDPR, cookies, and internal data standards.
• Delivering efficiency gains through automation, improved data workflows, and thoughtful technology choices.
• Collaborating closely with AI engineers, full stack engineers, and platform leads.

• Design, build, and maintain full-stack applications that support the firm’s internal AI platform and workflows.
• Develop scalable backend services, APIs, and data integrations to power AI-enabled features.
• Build intuitive, performant user interfaces that allow users to interact with complex, data-driven and AI-powered systems.
• Integrate AI and data capabilities into applications and internal tools.
• Collaborate closely with AI engineers, data engineers, and product stakeholders to deliver cohesive end-to-end solutions.
• Apply robust engineering practices across development, testing, deployment, monitoring, and observability.
• Optimise applications for performance, reliability, scalability, and security.
• Contribute to architectural decisions across application, platform, and integration layers.
• Troubleshoot and resolve issues across the stack in live environments.
• Document systems, patterns, and best practices to support maintainability and knowledge sharing.

Full Stack Engineer

AI Engineer

• Design, build, and maintain AI-driven applications and services used internally across the firm.
• Develop and deploy AI-powered workflows and systems that improve productivity, insight generation, and decision-making.
• Work closely with product, data, and domain experts to translate business problems into scalable technical solutions.
• Build reliable services, APIs, and pipelines to support AI use cases in production environments.
• Apply best practices for developing, evaluating, and operating AI systems in live environments.
• Integrate AI solutions with existing internal tools, data platforms, and workflows.
• Ensure solutions are secure, well-documented, observable, and aligned with internal governance standards.
• Contribute to architectural decisions and the long-term evolution of the firm’s internal AI platform.
• Stay current with developments in AI engineering practices and emerging approaches, bringing relevant innovation into the business.