Mgr Software Engineering

About the Role

Responsibilities

• Leadership
• Lead and develop a high-performing team of analytics engineers, data engineers and AI developers.
• Build a culture focused on innovation, engineering excellence and continuous improvement.
• Coach engineers in modern software engineering, AI-assisted development and best practices.
• Drive technical strategy and delivery across multiple concurrent initiatives.
• Collaborate with Product Managers, Architecture, Security, Finance and Business stakeholders.
• AI & Agentic Development
• Champion the adoption of enterprise AI technologies including:
• AI Agents
• Multi-agent workflows
• LLM orchestration
• Retrieval Augmented Generation (RAG)
• MCP (Model Context Protocol)
• Autonomous reporting
• Intelligent workflow automation
• AI-assisted software engineering
• Prompt engineering
• Agent evaluation frameworks
• Human-in-the-loop AI systems
• Identify opportunities where AI can automate manual reporting, business processes and operational decision making.
• Develop reusable AI capabilities that accelerate engineering productivity and improve customer outcomes.
• Enterprise Integration
• Lead integrations across enterprise systems including financial, operational and commercial platforms such as:
• ERP systems
• Financial platforms
• CRM platforms
• Contract management systems
• Operational applications
• Internal APIs
• Third-party SaaS platforms
• Event-driven architectures
• Streaming data platforms
• Design reliable and scalable ingestion frameworks supporting both batch and real-time processing.
• Data Engineering
• Drive engineering excellence across:
• Modern ETL / ELT
• Data pipelines
• Lakehouse architectures
• Data quality
• Metadata management
• Data governance
• Data observability
• Data lineage
• Performance optimisation
• CI/CD
• Infrastructure as Code
• Champion reusable engineering frameworks and platform standardization.
• Financial & Enterprise Reporting
• Lead delivery of enterprise reporting supporting Finance and executive stakeholders.
• Develop scalable semantic models supporting:
• Financial reporting
• Operational reporting
• Executive dashboards
• KPI scorecards
• Regulatory reporting
• Forecasting
• Planning
• Variance analysis
• Ensure reporting is trusted, performant and capable of supporting AI-powered natural language querying.
• Data Modelling
• Design enterprise-grade data models including:
• Kimball dimensional modelling
• Star schemas
• Snowflake schemas
• Data Vault concepts
• Semantic modelling
• Slowly Changing Dimensions
• Master Data Management
• Reference data
• Metrics modelling
• Partner with business stakeholders to ensure consistent enterprise definitions.
• Platform & Technology
• Drive adoption of modern cloud technologies including:
• Databricks
• Delta Lake
• Unity Catalog
• Azure Data Factory
• Azure Data Lake Storage
• Power BI
• Azure AI
• Azure OpenAI
• Python
• SQL
• Spark
• PySpark
• REST APIs
• Git
• Azure DevOps
• Terraform
• Docker
• Kubernetes
• Operational Excellence
• Develop monitoring and AI-powered observability across enterprise data products.
• Improve:
• Platform reliability
• Incident response
• Engineering productivity
• Operational intelligence
• Release automation
• Performance monitoring
• Use AI to proactively detect anomalies before they impact customers.

Required Qualifications

• Bachelor’s degree in Computer Science, Engineering, Information Systems or related discipline.
• Significant experience leading technical engineering teams.
• Experience delivering enterprise data platforms.
• Strong experience with cloud data engineering.
• Experience integrating enterprise source systems.
• Strong SQL and Python skills.
• Experience building scalable reporting solutions.
• Experience with dimensional modelling.
• Experience delivering financial reporting solutions.
• Experience working with APIs and enterprise integrations.
• Excellent stakeholder management and communication skills.
• Preferred
• Experience with one or more of:
• Databricks
• Azure Data Platform
• Microsoft Fabric
• Azure OpenAI
• Generative AI
• AI Agents
• MCP
• LangGraph
• LangChain
• Semantic Kernel
• Vector databases
• Knowledge Graphs
• RAG architectures
• Financial ERP systems
• SAP
• Oracle Financials
• Workday Financials
• Snowflake
• dbt
• Event Hub
• Kafka
• Leadership Competencies
• Successful candidates will demonstrate:
• Strategic thinking
• Technical leadership
• Innovation
• Customer obsession
• Bias for action
• Continuous improvement
• Coaching and mentoring
• Strong business acumen
• Data-driven decision making
• Cross-functional collaboration
• What You’ll Deliver
• AI-powered enterprise reporting capabilities.
• Intelligent agentic workflows that automate manual business processes.
• Modern integrations across enterprise source systems.
• Scalable financial and operational data products.
• Trusted semantic models supporting self-service analytics.
• High-quality engineering standards across the analytics platform.
• Increased engineering productivity through AI-assisted software development.
• Enterprise AI capabilities that transform how business users interact with data.
• Why Join Us
• This is a unique opportunity to help define the future of enterprise analytics engineering. You will lead the adoption of AI agents, intelligent automation, and modern cloud data platforms that will fundamentally change how enterprise reporting, financial analytics, and software engineering are delivered across the organisation.
• You will work at the intersection of AI, data engineering, cloud platforms, and business transformation, helping shape the next generation of intelligent enterprise systems.
• We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location.
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