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Key Responsibilities:

Data Architecture & Engineering

• 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.

Semantic & Contextual Modeling

• 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.

AI Integration & Enablement

• 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.

Infrastructure & DevOps

• 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.

Collaboration & Leadership

• 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.

Requirements:

Technical Skills

• 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.

Soft Skills

• 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).

Attitude & Aptitude

• 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.

Preferred Experience

• 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.