Director of Analytics Engineering

Director of Analytics Engineering

US Remote (CST/EST preferred; PST candidates should be comfortable with early meetings)

$200K–$230K base + bonus

We're working with a well-established, global travel management company on a Director of Analytics Engineering hire. The business is mid-way through a genuine data transformation, and this person will own the strategy, architecture, and delivery of a global analytics platform already serving thousands of clients worldwide.

This isn't a role for someone managing from a distance. My client needs a leader who has personally built and shipped data products, someone who's architected pipelines, modeled semantic layers, and knows what production-grade analytics at scale actually takes.

What you'd own:

• The multi-year roadmap for a global analytics platform, spanning internal BI and external client reporting
• End-to-end delivery of analytics data products, from pipeline architecture through semantic modeling to reporting
• Embedded analytics applications woven into the product experience, not bolted-on dashboards
• Technical direction on warehouse design (Snowflake, Azure DataLake), semantic modeling (LookML, dbt), and BI strategy (Looker, Power BI)
• A team of analytics engineers working across the full stack
• Data governance, observability, and reliability across the platform

What we're looking for:

• 10+ years in analytics engineering or data engineering, with 5+ years leading a team
• A track record of personally shipping production analytics products, both internal and client-facing
• Deep Snowflake experience, plus fluency across at least two major cloud providers
• Hands-on experience with Looker/LookML, dbt, and expert-level SQL
• Strong Python skills and real experience with data governance (RBAC, lineage, compliance)
• A diagnostic mindset, someone who digs into a slow query rather than delegating it blind
• Equally comfortable presenting to executives and talking tradeoffs with engineers

Nice to have: travel/hospitality/logistics experience, multi-tenant platform experience, infrastructure-as-code (Terraform, Docker, Kubernetes), ML/AI exposure, an advanced quantitative degree.