Manager of Advisory Services

Role overview

Strategy and delivery

• Own data engineering strategy, roadmap, and standards; lead proposals, client assessments, and phased modernization roadmaps.
• Lead global multi-squad delivery and large cloud data transformation programs, including risk, change, budget, and delivery planning.
• Set cloud architecture and security standards; modernize warehouses to lakehouse platforms and standardize Delta Lake optimization and data modeling.
• Oversee reliable batch and near-real-time pipelines and enable analytics, reporting, and data science solutions.
• Drive platform FinOps governance and cost optimization while balancing performance and scalability.

Platform, governance, and AI

• Standardize CI/CD with GitHub Actions, automated testing, release management, and infrastructure-as-code practices.
• Enforce access controls, encryption, secrets management, privacy, governance, metadata, lineage, quality, and regulatory compliance.
• Enable AI-ready datasets and guide use of Databricks AI Genie, Playground, and MLflow integration.
• Implement Snowflake Cortex or other native Generative AI tools and enable embeddings, vectorization, and RAG data patterns.

People and stakeholder leadership

• Hire, mentor, and develop managers and senior engineers; drive career development, skills planning, and succession.
• Communicate delivery status, risks, and outcomes to executive stakeholders.

Required qualifications

• 15+ years in data engineering or analytics platforms and 5+ years leading managers or large engineering teams.
• Deep experience with data services on a major cloud hyperscaler and cloud-native services across multiple cloud providers.
• Hands-on Databricks, PySpark, Delta Lake, and Snowflake experience; CI/CD implementation using GitHub Actions.
• Experience enabling teams on Databricks AI Genie, Playground, and MLflow.
• Proficiency in Python, SQL, and distributed data processing; familiarity with Snowflake Cortex and native AI capabilities.
• Solid understanding of AI/ML data lifecycle and governance, and awareness of RAG and vector-based data assets.
• Strong leadership, communication, stakeholder management, and project management skills for large-scale data engineering programs.