Mgr Software Engineering

RELX•Alpharetta, GA

1d•$115,400 - $192,300

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.
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.
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.
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.
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.
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.
Drive adoption of modern cloud technologies including: Databricks, Delta Lake, Unity Catalog, Azure Data Factory, Azure Data Storage, Power BI, Azure AI, Azure OpenAI, Python, SQL, Spark, PySpark, REST APIs, Git, Azure DevOps, Terraform, Docker, Kubernetes.
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.