Engineering Manager – AI & Data

About the RoleThe Engineering Manager – AI & Data is responsible for leading the design, development, and operation of enterprise AI, analytics, and data engineering platforms. The role ensures secure, scalable, and compliant delivery of AI, machine learning, and data solutions.ResponsibilitiesLead end-to-end delivery of AI/ML and data platforms (architecture, engineering, operations, and continuous improvement)Own data and AI reference architecture and engineering standards across Azure and Google Cloud (GCP)Lead lakehouse and data platform delivery using Databricks, Delta Lake, and enterprise data lake patternsDesign and govern enterprise data pipelines: data ingestion, ETL/ELT, batch/stream processing, and orchestrationDefine and enforce data modeling standards (dimensional, Data Vault, and medallion/layered approaches) to enable trusted analyticsEstablish and run MLOps/LLMOps practices: model lifecycle management, CI/CD, monitoring, evaluation, and guardrailsDeliver LLM-enabled solutions, including RAG patterns, tool/function calling, agentic architectures, and data agents for automationOversee use of Azure AI Foundry and Google Vertex AI for model development, deployment, and governed experimentationEnable enterprise BI and self-service analytics through Power BI, including semantic models, DAX standards, and governanceEstablish enterprise semantic layer and ontology standards (business glossary/metrics definitions) to ensure consistent reporting and AI understandingEnsure strong data governance, security, and PDPL compliance across platforms and productsImplement governed access, lineage, and data sharing using Unity Catalog (or equivalent) aligned with PDPL controlsManage and coach data engineering and ML engineering teams; set objectives, build capability, and drive delivery excellenceManage vendors, partners, and cloud providers; control cost, performance, and SLAsRequired SkillsDatabricks (Spark, Delta Lake, jobs/orchestration)Data lake / lakehouse architecture and designDatabricks Unity Catalog (data governance, access control, lineage)Ontology and semantic modeling (business glossary, knowledge graph concepts)Azure AI Foundry (model management, prompt/agent workflows)Google Vertex AI (training, deployment, pipelines)LLMs and agentic architectures (RAG, tool/function calling, evaluation/guardrails)Data agents and automation patternsPower BI (semantic models, DAX, governance; semantic layer)Cloud platforms: Microsoft Azure and Google Cloud (GCP)Data engineering: data ingestion, ETL/ELT, batch/stream processingData modeling (dimensional, Data Vault, medallion patterns)