Data Science & Engineering Manager

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)