1508 Full Stack Developer (Expert)

SUMMARY:
ESSENTIAL SKILLS:

• Expert software engineering capability with a strong record of designing, building, testing, deploying and
• operating production-grade enterprise solutions.
• Advanced Python and Java development, including clean code, object-oriented and functional design,
• concurrency, performance optimisation, packaging and dependency management.
• Strong data engineering capability across batch and event-driven ingestion, ETL/ELT, data transformation, data
• modelling, data quality, metadata, lineage and large-scale processing.
• Hands-on experience with APIs, microservices and integration patterns, including REST, GraphQL, messaging,
• Kafka and contract-first development.
• Cloud-native engineering experience, preferably AWS, including serverless, containers, storage, databases,
• queues, identity, networking and observability.
• AI engineering capability covering LLM-based applications, agents, retrieval-augmented generation, prompt
• and context engineering, tool integration, evaluation, guardrails and human approval gates.
• Ability to use AI-assisted development responsibly across specification, coding, review, testing, documentation
• and operations while retaining accountability for correctness and security.
• Strong systems thinking: understand end-to-end behaviour, dependencies, feedback loops, failure modes,
• trade-offs and the wider organisational impact of technical decisions.
• Out-of-the-box problem solving with the ability to simplify complex problems, challenge assumptions,
• prototype alternatives and convert successful approaches into reusable patterns.
• Engineering excellence across automated testing, secure coding, code review, CI/CD, Infrastructure as Code,
• version control and production-readiness practices.
• Ability to translate business and system requirements into maintainable architecture, technical designs,
• implementation plans and measurable engineering outcomes.
• Clear communication and technical leadership across multidisciplinary, distributed and multicultural teams.
• Adaptability to perform additional tasks under the Agile Working Model (AWM) Charter.

• Experience in automotive, mobility, manufacturing or a large multinational enterprise environment.
• Knowledge of enterprise data products, federated data platforms, Digital Twin concepts, semantic models,
• knowledge graphs and ontologies.
• Experience with Apache Spark, Kafka, SQL and NoSQL technologies, and modern lakehouse or warehouse
• patterns.
• Exposure to TypeScript/JavaScript and modern frontend frameworks such as Angular or React.
• Experience with Docker, Kubernetes, Terraform, GitHub Enterprise and GitHub Actions.
• Knowledge of observability, SRE, incident management, resilience engineering and service management
• practices.
• Understanding of privacy, information protection, data governance, retention, cross-border data handling and
• responsible AI controls.
• Ability to mentor engineers, lead technical design reviews and improve engineering standards across teams.
• High ownership, curiosity, integrity, resilience and a continuous-learning mindset.
• Willingness and ability to travel internationally when required.

POSITION INFO:
ROLE & RESPONSIBILITIES: Design, build, test, deploy and operate secure, scalable and maintainable DVF software, data products and platform capabilities. Own technical delivery from discovery and design through implementation, release, monitoring, support and continuous improvement. Develop high-quality Python and Java components for data processing, APIs, microservices, automation, integration and platform services. Engineer reliable batch and event-driven data pipelines with clear contracts, validation, observability, lineage and recoverability. Build AI-enabled solutions and reusable engineering accelerators, including agents, RAG patterns, tools, prompts, context packages, evaluations and guardrails. Apply specification-driven and AI-assisted development practices, using human approval gates for architecture, security, quality and production changes. Take a systems view of DVF, identifying cross-subsystem impacts, dependencies, bottlenecks, failure modes and opportunities to simplify the landscape. Challenge conventional approaches, rapidly prototype options and turn validated ideas into governed, reusable patterns and production solutions. Produce and maintain architecture views, technical designs, interface contracts, decision records, runbooks and implementation documentation. Implement automated unit, integration, contract, component, performance and regression tests as appropriate. Build and maintain CI\/CD pipelines, Infrastructure as Code, security scanning, dependency management and quality gates. Ensure solutions meet requirements for security, privacy, data governance, information protection, auditability, resilience and operational support. Use telemetry and operational feedback to improve reliability, performance, cost efficiency and developer experience. Lead code reviews and technical design reviews, mentor engineers and promote consistent engineering standards and reusable solutions. Collaborate with Product Owners, Systems Analysts, Architects, Data Engineers, Test Engineers, Operations and Security to resolve ambiguity and deliver measurable outcomes. Support PI planning and sprint execution by refining technical work, identifying dependencies and risks, and providing realistic implementation options. Communicate technical decisions, progress, risks and trade-offs clearly to technical and non-technical stakeholders. Contribute to the evolution of DVF into an AI-native, federated data-product organisation with shared platform, quality and AI enablement capabilities. QUALIFICATIONS\/EXPERIENCE: Relevant degree or diploma in Computer Science, Software Engineering, Information Technology, Data Engineering or a related field. Typically, seven or more years of relevant software engineering experience, including responsibility for complex production systems. Demonstrable expert-level delivery using Python and\/or Java, with strong data and integration engineering experience. Practical evidence of AI-assisted or AI-native engineering, including evaluation, governance and accountable human review. AWS, Java, Python, data engineering, architecture, DevOps, Kubernetes, Terraform, Agile or AI-related certifications are advantageous. Submit your CV to: and Subject line Role title