Data Engineer

Company Description

We are in Business for People, empowering people in service organizations with innovative Enterprise and Business software solutions. We’ve innovated and taken a new approach to delivering ERP that works for people. Self-driving, adaptive and intuitive software that is changing the way people work. Our solutions empower people and deliver a better people experience so people can spend time on meaningful high value work they live for.

Read more on our website about how we transform work and how people feel about it, so our customers and their people can thrive.

Job Description

We are seeking a forward-thinking Data Engineer to join our AI research team for ERP. This role is pivotal in designing and implementing the data foundation that enables intelligent agents, real-time and adaptive 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 that supports intelligent automation and orchestration within enterprise systems. Additionally, your expertise will play a vital role in advancing current and future data engineering initiatives across multiple teams and projects at Unit4.

Qualifications

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 &