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. About the Role 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. 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 d