Mgr Software Engineering

Are you ready to lead analytics engineers in turning AI from experimentation into trusted, production-grade capabilities-without compromising security or governance?

About the Business:

LexisNexis Risk Solutions is the essential partner in the assessment of risk. Within our Business Services vertical, we offer a multitude of solutions focused on helping businesses of all sizes drive higher revenue growth, maximize operational efficiencies, and improve customer experience. Our solutions help our customers solve difficult problems in the areas of Anti-Money Laundering/Counter Terrorist Financing, Identity Authentication & Verification, Fraud and Credit Risk mitigation and Customer Data Management. You can learn more about LexisNexis Risk at the link below,

https://risk.lexisnexis.com

About the Team:

This position provides leadership, management, direction, and vision to data engineering and/or employees including offshore contractors/consultants and interns needed to oversee statistical and analytical data analysis. The position works closely with technology peers, product and project leaders/managers, as well as directing the successful completion and delivery of respective data components and any other related deliverables. The position is additionally expected to report progress to senior management. Additional responsibilities may include oversight of the department budget, identifying and supporting talent, and defining resource requirements and allocations.

About the Team

The Enterprise Data Intelligence (EDI) team delivers enterprise-scale analytics, reporting, AI solutions, and data engineering capabilities supporting Finance, Commercial, Product, Operations, and Technology teams.

We are investing heavily in next-generation AI capabilities including:

• AI Agents
• Agentic software development
• Intelligent reporting
• Autonomous monitoring
• Enterprise integrations
• LLM-powered analytics
• Natural language data experiences
• Data product engineering

This role will help shape how AI transforms both engineering productivity and business decision making., We are seeking an experienced Manager - AI Integration & Agentic Analytics Engineering to lead the development of intelligent enterprise data products, AI-powered reporting solutions, and modern integrations across our business platforms.

This individual will lead a team responsible for connecting enterprise source systems into our cloud data platform while championing the adoption of AI Agents to automate business processes, improve engineering efficiency, and deliver next-generation analytical capabilities.

The ideal candidate combines strong leadership with hands-on technical expertise across data engineering, financial systems, enterprise integrations, dimensional modelling, reporting architecture, and modern AI technologies.

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
• Data quality
• Incident response
• Engineering productivity
• Operational intelligence
• Release automation
• Performance monitoring

Use AI to proactively detect anomalies before they impact customers., * 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.

• 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