AllTrails is the app for exploring the outdoors, helping people find their next adventure. The Data Science & Engineering Manager will lead the Data Science & Engineering team, focusing on data infrastructure, governance, and the development of machine learning solutions to enhance internal operations and member experiences.

Responsibilities:

Manage & Grow the Team: Lead, hire, and coach a team of data engineers and applied data scientists while fostering team health, professional development, and high performance through active coaching and performance management
Stakeholder Alignment: Partner with cross-functional leaders to translate business needs into technical requirements, prioritize initiatives, and balance long-term platform investments with near-term delivery goals
Domain & AI Innovation: Serve as a key thought partner on leveraging data, AI, and automation to enhance AllTrails' internal productivity, trail content, map quality, and member personalization experiences
Pipeline & ML Architecture: Drive the architecture and delivery of scalable batch and streaming pipelines, while scaling the productionization of ML models and algorithmic systems into dependable engineering solutions
System Operations & Reliability: Oversee orchestration, transformation, alerting, and data flow across the organization—troubleshooting complex performance bottlenecks and system failures to ensure high system uptime
Governance & Self-Service: Champion data reliability, quality, privacy, compliance, and cost efficiency while building tools for data cataloging, documentation, and self-service to democratize data assets
Velocity & Continuous Improvement: Create organizational leverage by improving time-to-insight, debugging speed, and deployment velocity, while continuously evaluating new tools and technologies to elevate data capabilities

Requirements:

5+ years in data engineering with 3+ years managing data engineering, machine learning, and data science teams with a focus on coaching senior ICs, career development, and building an inclusive, accountable team culture
An adaptable, self-motivated leader who thrives in fast-paced, ambiguous environments—balancing strategic vision with hands-on technical guidance, humility, empathy, and a passion for the outdoors
Hands-on experience managing GCP data infrastructure, enforcing data governance, and maintaining production-grade data access patterns, storage, caching, and optimization standards
Extensive proficiency in transforming high-volume datasets in columnar SQL warehouses (BigQuery preferred, or Snowflake) using ELT tools (Dataform, dbt) and orchestrators like Apache Airflow
Proven expertise designing complex SQL/Python pipelines using parallelized processing frameworks (Dataflow, Spark, or similar) and core data modeling techniques (dimensional, star schema)
Deep familiarity with software engineering best practices, including Docker/Kubernetes, CI/CD pipelines, monitoring/observability tools (Datadog, New Relic, etc.), code reviews, and monthly on-call rotation
Track record of guiding teams through the full ML lifecycle (MLOps) to support applied use cases like recommendation, ranking, personalization, and classification engines
Skilled at leading across both platform foundations and applied data products, with the business judgment to prioritize competing stakeholder needs
Proven ability to translate complex business requirements into technical specs, collaborating effectively with stakeholders to own projects from inception to completion
Experience working with geospatial data, maps, tiling, graph databases, and/or routing
Experience working in a multi-cloud environment (GCP + AWS)
Experience working at a B2C company
Skilled at managing infrastructure as code (e.g. Terraform)

Agentic AI Engineer (Healthcare Domain Must)

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