Director of AI

Go Cadre•San Diego, CA

About The Position

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. Automotive industry experience is not required. What matters is retail orientation: CRM, customer-facing AI, multi-location operational complexity, and a genuine curiosity about the business you're joining.

Requirements

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

Nice To Haves

Spanish language proficiency is strongly preferred. Sunroad operates dealerships in Tijuana and Mexico City and plans to build a data and AI team in Latin America. Candidates with experience managing engineering or analytics resources in Latin America, and who can work directly with that team in Spanish, will receive priority consideration.
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.
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.
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.
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.
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.
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.
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.

Responsibilities

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
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
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
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