The Role
The Director of AI is responsible for building the AI infrastructure and strategy, leading a team, and implementing AI solutions across the organization. This includes overseeing data governance, cloud data architecture, and customer-facing AI tools while managing stakeholder relationships and driving team performance.
Summary Generated by Built In
Director of AI
The Role
What You'll Do
Who You Are
What Sets You Apart
Compensation
Build the AI strategy that powers the company
This is a foundational leadership role. You will be responsible for building the AI infrastructure, the team, and the strategy from scratch. This role reports directly to the President, with dotted line relationships to other executive staff, and may evolve as the function and organization grow.
This is primarily a coach role with a hands-on edge. You set the strategy, own the architecture decisions, and lead the team that executes. You are not writing pipelines every day, but you are close enough to the work to design the right solutions, spot problems early, and roll up your sleeves when it matters. Someone who can only manage and someone who can only build are both the wrong fit.
What matters is retail orientation: CRM, customer-facing AI, multi-location operational complexity, and a genuine curiosity about the business you're joining.
AI STRATEGY, INFRASTRUCTURE & DEPLOYMENT
- Own the AI and data strategy covering governance and quality through to architecture, accessibility, and deployment across all brands and locations
- Assess and remediate current data infrastructure. Much of it needs cleanup before it can power reliable AI, and DMS integrations, CRM data, inventory systems, and financial reporting all need a critical eye
- Architect and oversee cloud-native data warehouse solutions using Snowflake, BigQuery, or Redshift, with a focus on scalability, cost efficiency, and cross-location accessibility
- Design and oversee modern ELT/ETL pipelines using tools such as dbt, Airflow, Fivetran, and Python-based ingestion frameworks, ensuring data is clean, documented, and reliable
- Define and enforce data governance standards, including ownership, quality controls, lineage tracking, access management, and documentation
- Identify, prioritize, and execute AI and automation initiatives with clear ROI tied to gross margin, F&I performance, service throughput, customer retention, and operational efficiency
- Lead customer-facing AI implementation across CRM, sales, and service, including lead scoring, follow-up automation, and AI-assisted F&I, ensuring tools are configured correctly and actually driving results
- Lead end-to-end delivery of AI solutions, from discovery and scoping through build, adoption, and optimization
- Evaluate existing AI tools for proper implementation and redundancy. Many tools are already purchased; your job includes making sure they're actually working
- Evaluate and manage vendor and technology partner relationships across the AI and data stack
- Build AI governance frameworks that ensure responsible, auditable, and scalable use of AI across the organization
STAKEHOLDER LEADERSHIP
- Serve as the primary AI advisor to the executive team, translating technical strategy into clear business decisions
- Partner with other departmental leads to understand real operational pain points and design solutions that work in their daily workflows, not just on paper
- Run executive-level reporting and strategy sessions, presenting progress, tradeoffs, and recommendations with clarity and confidence
- Drive change management and adoption. You are accountable for whether people actually use what gets built, not just whether it was built
TEAM BUILDING & ORGANIZATIONAL DEVELOPMENT
- Build and lead a lean, high-performing AI team. We're looking for an efficient strike team, not a large org. The right person executes without needing to build a department beneath them first
- Define the organizational structure: what to build internally, what to hire for in Latin America, and where to rely on external partners — keeping the footprint small and the output high
- Work alongside the existing BI team to strengthen capability, close gaps, and establish a clear path forward for the data organization
- Manage team performance, provide coaching and mentorship, and build a culture of accountability and continuous improvement
EXECUTION & DELIVERY
- Manage a portfolio of concurrent AI initiatives, balancing foundational infrastructure work with high-visibility quick wins
- Track and communicate progress, business impact, and ROI using clear metrics and structured reporting
- Ensure that what gets built is reliable, documented, and maintainable. Systems should outlast any individual on the team
- 8–12+ years in data engineering, data architecture, or a closely related technical role, with at least 3–5 years in a senior leadership position with cross-functional accountability
- Strong data infrastructure and data cleanup experience is required. You have architected and built pipelines, fixed broken data environments, and established governance in organizations where data quality was a real problem, not just a future aspiration
- Retail, CRM, and customer-facing implementation experience is strongly preferred. You have worked in multi-site retail, automotive-adjacent, or similar consumer-facing businesses where CRM data, lead management, and customer lifecycle tools are central to the operation
- Hands-on experience with modern data stack components: cloud data warehouses (Snowflake strongly preferred; BigQuery or Redshift also valued), ELT/ETL tools (dbt, Fivetran, Airflow or equivalent), BI tools (Tableau, Power BI, Looker), and cloud platforms (AWS, Azure, or GCP)
- Deep proficiency in SQL and Python, with demonstrated experience building and maintaining production data pipelines and transformation layers
- Practical experience deploying AI and ML solutions in production. Real systems that real people use, not proofs-of-concept that never left the sandbox
- Fluent in both technical depth and business communication, equally comfortable architecting a data warehouse and presenting a data strategy roadmap to executive leadership
- Experience hiring, developing, and managing technical data engineering teams, including distributed or offshore resources, with accountability for their performance, growth, and output quality
- Strong stakeholder management skills, including the ability to earn trust with operators, store-level leaders, and executives who did not ask for a data and AI function
- Comfortable managing a portfolio of concurrent initiatives. You create structure where there isn't any and you don't need a perfectly defined mandate to move
Spanish language proficiency is strongly preferred, as this role includes building and leading engineering and analytics resources in Latin America. Candidates with that experience will receive priority consideration
01
You've built from zero. Former founder, early data engineering leader, or someone who has operated like one inside a larger org. You know what it means to own the whole problem before there's a team to delegate it to.
02
You've cleaned up messy data environments. You have a track record of coming into organizations where the data infrastructure was broken or immature and actually fixing it, not just documenting the gaps and recommending a future-state architecture. You've done the remediation work yourself.
03
You have deep Snowflake expertise. You have architected and delivered Snowflake-based data platforms in production environments. Experience with dbt, Fivetran, Airflow, or similar modern stack tools is a meaningful differentiator. Familiarity with Snowflake Cortex, Snowpark, or complementary platforms like Databricks or BigQuery is a plus.
04
You understand retail and CRM at a functional level. You have implemented or optimized customer-facing AI tools in a retail context, including lead management, CRM workflows, service automation, or similar. You know how these tools fail in practice and what it takes to make them actually work.
05
You've managed teams in Latin America. You have experience building and leading engineering or analytics resources in Latin America, ideally in Spanish, and you understand how to make distributed teams productive without constant oversight.
06
You're a vendor critic, not a vendor fan. You can audit a tech stack honestly, identify redundancy, and tell leadership what they don't need to buy. Automotive experience is not required, but a genuine curiosity about the business and comfort learning a new domain quickly is.
07
You're a starter and a finisher. You generate momentum and you close things out. You don't hand off initiatives at the hard part, and you don't lose energy after the kickoff. Both modes, not one. If you build for impact, operate without excuses, and want to own something from the ground up, we want to talk.
Compensation
The base pay range for this role is $160,000 – $210,000 per year.
Top Skills
Airflow
BigQuery
Dbt
Fivetran
Python
Redshift
Snowflake
SQL
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The Company
What We Do
Cadre is an AI strategy and integration firm focused on helping organizations unlock the power of AI to drive measurable business results that grow revenue, increase EBITDA, and unlock scalability. We implement best-in-class AI tools and technologies to AI-Enable your business and create a lasting competitive advantage. Our combination of entrepreneurial experience, strong operational understanding and technical AI expertise enables us to help our clients integrate cutting-edge AI technologies to drive efficiency, productivity, and growth. As a team we take an “ownership mindset” to our clients business, asking ourselves “what moves would I make if this was my business” and are committed to transforming your company into a “future-proof” organizations by leveraging the power of AI to achieve operational excellence and enhanced profitability and putting you ahead of your competitors and industry.
