Jobs at Appnovation

Leads technical strategy and architecture for a global business division, defining target-state architecture, standards, integration patterns, and technology roadmaps. Represents the division on architecture reviews, aligns initiatives with enterprise platforms, establishes reusable capabilities, and advises senior stakeholders. Provides hands-on technical guidance, mentorship, and forward-deployed engineering support while advancing AI-enabled delivery, security, reliability, governance, scalability, and operational excellence.

Own the technical strategy, target architecture, standards, and roadmap for a global digital platform. Lead engineering teams, guide architecture and integration decisions, partner with senior stakeholders, and apply AI-enabled delivery responsibly. Establish requirements for security, reliability, governance, observability, scalability, and operational excellence while developing technical talent and driving reusable digital capabilities.

Build and register domain-grounded data agents over governed Databricks and Snowflake datasets. Perform lakehouse migrations to open formats such as Apache Iceberg, curate catalogue metadata, and support data-contract workflows. Partner with business stakeholders and subject-matter experts to validate data assets. Implement advanced SQL and PySpark pipelines, orchestration, data quality, observability, lineage, masking, and row- and column-level security. The role also supports vector search, embeddings, cloud data onboarding, and regulated-domain use cases.

Own the end-to-end architecture of a governed, zero-copy, cross-cloud data platform spanning Databricks, Snowflake, and other data sources. Define architecture standards, reference designs, metadata and catalog integrations, data contracts, access controls, and agent- or MCP-based data consumption patterns. Translate product vision into technical specifications, guide engineering decisions, ensure enterprise alignment, and communicate complex trade-offs to technical and business stakeholders in a consulting environment.

Leads enterprise web and CMS digital transformation projects from discovery through post-launch optimization. Manages cross-functional teams, client relationships, scope, budgets exceeding £1M, schedules, profitability, resources, risks, integrations, content migration, governance, and reporting. Supports pre-sales activities, including RFPs, scoping, and statements of work, while driving continuous delivery improvement and identifying growth opportunities.

Build AI-augmented, full-stack applications from concepts and prototypes through production, with emphasis on frontend excellence. Create accessible, responsive React, Vue, and Nuxt interfaces; develop APIs and backend services; manage SQL and NoSQL databases; implement authentication, caching, performance, and scalability strategies. Use AI design tools, Figma, design systems, and specification-driven workflows while collaborating with designers and innovation teams.

Build full-stack AI and data-platform applications, including Python backend automation, agent-enabled workflows, MCP servers, metadata and data-contract tooling, and platform discovery interfaces. Design APIs, asynchronous services, schemas, microservices, testing, and CI/CD pipelines while integrating LLMs, graph data, cloud infrastructure, and governance platforms. Contribute to architecture, scalability, security, code quality, and client-focused consulting delivery.

Manage multiple concurrent client projects and Scrum teams, coordinating sprints, stand-ups, retrospectives, backlog refinement, budgets, timelines, resources, risks, and deliverables. Coach teams in Agile practices, remove impediments, resolve conflicts, facilitate stakeholder communication, track velocity and costs, and improve delivery processes while ensuring high-quality outcomes.

Lead rapid concept-to-production work using AI design tools and modern full-stack frameworks. Build accessible, performant frontends and robust backends, implement APIs, auth, caching, and databases, and collaborate with designers and AI builders to maintain design systems and deliver production-ready applications.

Lead advanced analytics and ML initiatives to build predictive models, drive AI governance, architect scalable data strategies and lakehouse solutions, enable rapid prototyping and MLOps, embed with business teams to discover problems and deliver measurable business impact across cross-functional stakeholders.

Lead end-to-end ML and data strategy across complex enterprise systems. Design and implement advanced statistical and machine learning solutions (including NLP and computer vision), architect data governance and lakehouse/warehouse integrations, drive AI evaluation and governance, and rapidly prototype solutions. Influence multiple teams, translate business needs into technical solutions, and discover ambiguous problems through user immersion to deliver measurable business impact.