AI Data Engineer

Summary

Join Unit4's AI research team as a forward-thinking AI Data Engineer, playing a pivotal role in designing and implementing the data foundation for intelligent agents and real-time enterprise systems. You will work at the intersection of data architecture, semantic modeling, and AI integration, enabling innovation across AI, analytics, and enterprise automation. Your primary responsibility will be AI research supporting intelligent automation and orchestration within enterprise systems. This role involves advancing data engineering initiatives across multiple teams and projects. You will collaborate with architects, data scientists, AI engineers, and product leads. Unit4 offers a fast-paced, high-growth, people-centric environment with numerous benefits and development opportunities.

Requirements

• Data Architecture & Engineering: Expertise in data Lakehouse architecture (Delta Lake, Databricks, Spark SQL), dbt, DLT, Airflow
• Streaming & Event Systems : Strong experience with real-time data processing (Apache Kafka, Flink, Spark Streaming), event modeling, schema evolution
• Semantic & Metadata Modeling : Proficiency in semantic modeling, ontology engineering, metadata management, knowledge graphs, Unity Catalog, GraphRAG, LlamaIndex
• Data Governance : Expertise in data privacy, security and compliance (e.g. GDPR)
• Cloud & DevOps : Experience with cloud platform (Azure/AWS/GCP) and IaC tools (Terraform, Kubernetes, Docker), CI/CD for data and supporting AI workloads
• AI Integration : Familiarity with AI integration patterns, operations and automations, model lifecycle management, real-time feature pipelines, including MCP & A2A protocols and context-aware APIs
• Programming : Python, .NET (C#), SQL, and optionally Scala or Java
• Collaboration & Communication : Work effectively across architecture, AI, product, and engineering teams
• Agile Mindset : Comfortable with iterative delivery, version control, and DevOps practices
• Governance Awareness: Comprehensive understanding of, and adherence to, organizational best practices concerning data privacy, security, and compliance requirements (such as GDPR)
• System Design Thinking : Ability to architect end-to-end data platforms with scalability, latency, and reliability in mind
• Problem Solving : Strong debugging skills in distributed systems and performance tuning
• Curiosity & Learning : Eagerness to explore emerging technologies in AI, streaming, and semantic modeling
• Adaptability : Comfortable navigating ambiguity and evolving project scopes
• Ownership : Proactive in identifying gaps, proposing solutions, and driving initiatives forward
• Ethical Thinking : Awareness of responsible AI practices and data ethics in production systems

Responsibilities

• Design and implement scalable data lakehouse architectures using Delta Lake and Databricks
• Support and contribute to the evolution of the Unit4’s data platform and related initiatives
• Define and enforce data lifecycle management, data contracts, and metadata standards
• Design and develop real-time data pipelines and streaming architectures using Kafka, Spark Streaming, or Flink
• Ensure data quality, lineage, and governance across structured and unstructured sources
• Model, develop and maintain ontologies and semantic models to support AI agents and context-aware data access and queries
• Model business flows and relationships using knowledge graphs and contextual metadata (e.g., GraphRAG, LlamaIndex)
• Collaborate on the implementation of Model Context Protocol (MCP) and Agent2Agent (A2A) for agent interoperability
• Enable AI agents to access, interpret, and act on ERPx data through well-structured APIs and semantic layers
• Support the integration of AI/ML models into event-driven architectures,  microservices and agentic systems
• Collaborate with AI engineers to design AI workloads and meet AI requirements
• Design cloud-native, scalable infrastructure for data serving AI workloads (Azure, AWS, GCP)
• Implement CI/CD pipelines for data using tools like Bicep, Airflow, Terraform
• Ensure observability, monitoring, and compliance in data and AI systems
• Collaborate with AI engineers to define retraining triggers, model drift detection, and feedback loops
• Work closely with architects, data scientists, AI engineers, and product leads to align data architecture with business goals
• Participate in design and architectural reviews, roadmap planning, and research cross-functional discussions
• Mentor junior engineers and contribute to knowledge sharing across teams

Preferred Qualifications

• 5+ years in software engineering with a strong foundation in modern engineering practices
• Experience in enterprise software, ERP systems, enterprise-scale data platforms and process automation
• Familiarity with data product thinking and self-service data platforms
• Hands-on experience with Lakehouse architectures and real-time data pipelines
• Proficiency in semantic modeling, ontology engineering, business flows and relationships using knowledge graphs and contextual metadata, data lifecycle management and data contacts
• Strong knowledge of streaming architecture design patterns
• Understanding of AI/ML lifecycle, AI agent frameworks and protocols for agent interoperability

Benefits

• A culture built on trust - giving you the freedom and autonomy to be successful
• Balance - with our uncapped time off policy, remote working opportunities and Global Wellbeing Days when the whole company can switch off and prioritize well-being
• Talented colleagues, role models and mentors - work, learn and be inspired by some of the best talent in the software industry
• A commitment to sustainability - with initiatives such as our Act4Good program, a way for everyone at Unit4 to come together and engage in actions that benefit society and the planet
• A safe and inclusive working environment – supported by our Employee Resource Groups, which are open to all and include Women at Unit4, Pride at Unit4, Mental Health and Access at Unit4, and People of Color at Unit4