Summary
The AI Engineering Lead defines and operationalizes enterprise data architecture, governance, and platform strategy to support scalable AI, analytics, and digital initiatives. This leader enables trusted, secure, reusable, AI-ready data assets and works across engineering, governance, architecture, security, and product teams to advance enterprise AI adoption.
Responsibilities
- Define enterprise data architecture standards and cloud-native platform strategies using Azure, Databricks, and modern distributed data frameworks.
- Lead data readiness and onboarding for AI use cases, developing reusable data products and validating quality, lineage, governance, and operational readiness.
- Establish standards for data quality, metadata, cataloging, stewardship, retention, compliance, and platform observability.
- Oversee scalable data ingestion, streaming, transformation, and engineering services while driving CI/CD, DataOps, testing, and operational excellence.
- Align AI and data organizations, advise business and technology leaders, and build and mentor high-performing engineering teams.
Requirements
- Bachelor’s or master’s degree in a relevant technical discipline, or equivalent experience.
- Significant experience in enterprise data engineering, cloud data platforms, distributed systems, and large-scale data architecture.
- Expertise in Azure data services, Databricks, Lakehouse architecture, Delta Lake, and scalable ETL/ELT and streaming pipelines.
- Strong knowledge of data governance, quality, lineage, metadata, semantic modeling, and reusable data product frameworks.
- Experience enabling AI and machine learning through governed data foundations, with understanding of vector-ready architectures, RAG, and unstructured data processing.
- Demonstrated engineering leadership, team development, stakeholder influence, and focus on platform reliability and operational excellence.
