AVP II – AI and Data Engineering

Role Summary

Business Partnership and Value Delivery

• Partner with business leaders and users to understand problems, desired outcomes, operating context, and constraints.
• Maintain a prioritized roadmap balancing customer value, strategic alignment, risk, feasibility, and team capacity.
• Define benefit hypotheses, success measures, and adoption plans, and track operational, financial, risk, and customer outcomes.
• Advise senior stakeholders and governance forums on options, trade-offs, dependencies, progress, and decisions.

Team Leadership and Delivery

• Lead, coach, and develop a multidisciplinary software, AI, and data engineering team.
• Own delivery from discovery and planning through build, testing, release, adoption, and support.
• Establish product and agile practices that enable feedback, prioritization, and reliable execution.
• Plan capacity, remove blockers, and coordinate specialists, delivery partners, and vendors.

AI and Software Product Delivery

• Guide secure, production-ready software products, AI-enabled applications, automation, and agentic workflows.
• Translate business outcomes into product features, user stories, acceptance criteria, release plans, and feedback loops.
• Ensure engineering practices cover architecture, APIs, integration, testing, code review, CI/CD, cloud deployment, security, and maintainability.
• Oversee AI evaluation, human oversight, responsible-use controls, monitoring, and lifecycle management.

Data Engineering and Databricks

• Own reliable data pipelines, reusable data products, and governed Databricks datasets for applications, analytics, and AI.
• Provide technical direction across lakehouse architecture, batch and streaming ingestion, SQL, Python, dbt, modeling, and integration.
• Champion data quality, lineage, observability, access control, privacy, and governance.
• Review designs and code, challenge technical decisions, support incidents, and guide complex engineering work.

Governance and Operations

• Establish governance covering scope, architecture, risk, change, release readiness, benefits, and operational ownership.
• Align with Enterprise Architecture, Cyber Security, Data Governance, Technology, and business teams.
• Ensure production services have ownership, support models, service objectives, monitoring, incident practices, lifecycle plans, and transparent run costs.
• Build trusted relationships and shared accountability across technical and non-technical stakeholders.

Requirements

• Significant experience delivering software, data, and AI capabilities in complex organizations with several years leading technical teams.
• Proven people leadership and experience creating inclusive, high-performing teams.
• Experience taking ambiguous problems through discovery, prioritization, delivery, adoption, and measurable benefit realization.
• Strong product and delivery management skills, including roadmaps, agile practices, dependencies, risks, and governance.
• Broad software engineering knowledge across cloud-native applications, APIs, integration, testing, CI/CD, security, reliability, and support.
• Practical Databricks and data engineering knowledge, including lakehouse patterns, pipelines, SQL, Python, dbt, modeling, and governance.
• Working knowledge of applied AI, machine learning, LLM solutions, evaluation, observability, responsible AI, and production lifecycle.
• Relevant degree or equivalent practical experience in software engineering, data, AI, computer science, or a related discipline.

Company Culture

Application and Equal Opportunity