Manager, AI Integration & Agentic Analytics Engineering

About the role

Lead a team of analytics engineers, data engineers and AI developers to build intelligent enterprise data products and AI‑powered reporting solutions. The role bridges data engineering, financial systems, and modern AI technologies to deliver production‑grade capabilities while ensuring security and governance.

Key responsibilities

• Lead and develop a high‑performing team, fostering a culture of innovation, engineering excellence and continuous improvement.
• Champion adoption of AI agents, multi‑agent workflows, LLM orchestration and autonomous reporting to automate business processes.
• Design and implement reliable, scalable ingestion frameworks for batch and real‑time data across ERP, CRM, financial and SaaS platforms.
• Drive modern ETL/ELT pipelines, lakehouse architectures, data quality, governance and observability.
• Deliver enterprise‑grade reporting, semantic models and KPI dashboards for finance and executive stakeholders.
• Define and enforce data modelling standards including dimensional, star, snowflake and Data Vault approaches.
• Promote cloud technologies such as Databricks, Azure Data Factory, Delta Lake and Azure OpenAI.
• Develop AI‑powered monitoring and anomaly detection to improve platform reliability and engineering productivity.

Required profile

• Bachelor’s degree in Computer Science, Engineering, Information Systems or related field.
• Significant experience leading technical engineering teams and delivering enterprise data platforms.
• Strong stakeholder management and communication skills.

Required skills

• Python and SQL programming.
• Databricks, Delta Lake, Unity Catalog.
• Azure Data Factory, Azure Data Lake Storage, Power BI.
• Azure AI, Azure OpenAI, LLM orchestration.
• Spark, PySpark, CI/CD, Infrastructure as Code (Terraform).
• Experience with APIs, event‑driven architectures, Kafka, Event Hub.
• Dimensional modelling, Kimball, Data Vault, semantic modelling.