AVP, Data, Analytics & AI Engineering

Description

The company builds an AI-powered scenario planning and analysis platform that helps businesses make faster, better-informed decisions.

Customers include many Fortune 50 firms and more than 2,400 global companies.

The team is united by a commitment to customer success and a culture that celebrates its wins.

This executive role sets the strategy for, and leads the design, build-out and growth of, the company's enterprise data platforms and AI solutions.

The leader turns enterprise data into secure, compliant intelligence that automates and guides decisions across the company, linking modern cloud data engineering with newer AI approaches such as LLMOps and agentic workflows. Engineering work must produce measurable business value while meeting strong standards for data security, governance and responsible AI.

You will work directly with the CFO, CRO, CHRO, CIO and product leaders. The role is critical and demands enterprise transformation leadership, discretion, and the ability to juggle several priorities in a fast-paced, sales-driven SaaS setting while building a high-performance engineering team.

Responsibilities

• Build, mentor and motivate a team spanning data engineering, analytics engineering and AI engineering.
• Set and govern the enterprise agentic AI strategy, and deploy multi-agent frameworks and LLM-powered tools at scale.
• Own the technical direction for GenAI and agent work, including MCP, RAG, embeddings and vector search, tool calling, agent architecture, evaluation, grounding and orchestration.
• Build multi-agent AI systems for complex, multi-step workflows that connect to ERP, CRM and ITSM systems and automate high-value decisions end to end.
• Create internal AI tools and agents, such as an AI Finance Bot and domain-specific LLM applications, and set the roadmap for next-generation agent platforms as new capabilities mature.
• Lead advanced analytics and decision intelligence, covering predictive analytics, propensity modelling, forecasting, experimentation, causal reasoning and optimization.
• Run data governance and AI readiness: quality, lineage, observability, catalog, access controls, PII handling, retention and AI-specific governance.
• Supply business context for a semantic and context architecture that includes a metrics layer, business ontology, entity relationships, metadata and knowledge graphs or semantic models.
• Work with business units, product, security, legal and compliance teams to deliver data and AI solutions that support enterprise goals.
• Move the company beyond pipelines that feed dashboards toward a modern data architecture.

Requirements

• A bachelor's or master's degree in computer science, data engineering, AI/ML, mathematics or another highly quantitative field.
• 12+ years of leadership in data and software engineering, including at least 5 years in a senior manager or executive role leading cross-functional teams.
• Solid working knowledge of cloud data platforms such as Snowflake, Databricks, Vertex AI and AWS Sagemaker.
• Experience moving teams to a modern data architecture, including deep data warehouse and lakehouse work (AWS Redshift, Databricks), streaming and batch processing, APIs, data products, ELT, CDC, and both structured and unstructured data.
• A proven record of deploying ML models to production, including feature engineering, model serving, monitoring and MLOps.
• The ability to explain complex agentic systems, context-engineering strategies and distributed database design in clear, business-focused terms to non-technical audiences and executive boards.

Conditions

Hiring decisions do not depend on gender identity or expression, sexual orientation, religion, ethnicity, age, neurodiversity, disability status, citizenship or other personal traits.

The hiring process includes an extensive interview with a recruiter and a hiring manager, by video or in person.

Offers are usually made verbally first by the internal recruitment team, then confirmed in writing.