AVP, Data, Analytics & AI Engineering

Description

We build an AI-driven platform for scenario planning and analysis that helps business leaders make faster, better-informed decisions and stay ahead of competitors.

More than 2,400 companies worldwide use the platform, and many of them are Fortune 50 firms.

Teams share a focus on customer success and celebrate wins of every size, and they welcome a range of perspectives.

This executive role owns the strategy, architecture, build-out and scaling of enterprise data platforms and AI solutions.

The goal is to turn company data into secure, compliant, high-performance intelligence that automates work and supports decisions across the business.

The role connects modern cloud data engineering with newer AI practices such as LLMOps and agentic workflows, and ensures engineering standards produce measurable business value alongside strong data security, governance and responsible AI.

You will work directly with the CFO, CRO, CHRO, CIO and product leaders, juggling several priorities at once in a fast-paced, sales-driven SaaS setting, and you will need to exercise discretion.

You will also build a high-performance engineering team.

Responsibilities

• Lead advanced analytics and decision intelligence, including predictive models, propensity scoring, forecasting, experimentation, causal analysis and optimization.
• Own the enterprise agent strategy, covering MCP, RAG, embeddings and vector search, tool calling, agent design, evaluation, grounding and orchestration.
• Set and govern the enterprise agentic AI strategy, and roll out multi-agent frameworks and LLM tools at scale.
• Design and ship enterprise multi-agent systems for multi-step workflows, connected to ERP, CRM and ITSM so high-value decisions run end to end.
• Create internal AI tools and agents, such as an AI Finance Bot and domain-specific LLM applications, and plan the roadmap for next-generation agentic platforms as new capabilities mature.
• Run data governance and AI readiness, covering data quality, lineage, observability, catalog, access controls, PII handling, retention and AI-specific governance.
• Shape the semantic and context architecture, including the metrics layer, business ontology, entity relationships, metadata, and knowledge graphs or semantic models, with business context.
• Build, mentor and motivate teams in data engineering, analytics engineering and AI engineering.
• Work with business units, product, security, legal and compliance teams to deliver AI and data solutions that meet enterprise goals.
• Move the company past pipelines that only feed dashboards and into a modern data architecture.

Requirements

• Hold a bachelor's or master's degree in computer science, data engineering, AI/ML, mathematics or another highly quantitative field.
• Have 12+ years of leadership in data and software engineering, including at least 5 years as a senior manager or executive leading cross-functional teams.
• Be strongly familiar with cloud data ecosystems such as Snowflake, Databricks, Vertex AI and AWS Sagemaker.
• Have experience moving teams to a modern data architecture, with deep knowledge of data warehouses and lakehouses (AWS Redshift, Databricks), streaming and batch processing, APIs, data products, ELT, CDC, and both structured and unstructured data.
• Have a proven record of putting AI/ML models into production, including feature engineering, model serving, monitoring and MLOps.
• Be able to explain complex agentic systems, context engineering and distributed databases to non-technical audiences and executive boards in clear, business-focused terms.

Conditions

All candidates are considered regardless of gender identity or expression, sexual orientation, religion, ethnicity, age, neurodiversity, disability status, citizenship or other characteristics.

Interviews include a recruiter and the hiring manager, by video or in person, and offers are made verbally where possible, then confirmed in writing.