Job
tzajni toj t-xnaq´tzb´il
Data Science & Engineering Manager
T-xilen t-xjel
Tqanil
- T-xilen aq´untl
- Toj tkyaqil amb´il
- najchaq
- Ja´lo
- Empresa
- AllTrails
Tchik'b'ab'il
AllTrails is the app for exploring the outdoors. We help people spend more time outside by connecting them to their next adventure. Home to the world’s largest outdoor community, with members and trails around the globe, you can find AllTrails in the Apple App Store, Google Play Store, and at alltrails.com.
Every day, our team works to get more people outside and help our members have better experiences on the trail. We lead with our values–positivity, innovation, humility, adaptability, inclusivity, and stewardship–in everything we do. Join us!
This is a U.S. based remote position. While this role does not require in-person attendance, we have a strong preference for candidates based in or near one of these cities: San Francisco CA, Portland OR, Seattle WA, Denver CO, and New York NY. Being in one of these cities creates opportunities to connect with teammates through Trail Days, co-working sessions, and local gatherings while still having the flexibility to work remotely. San Francisco employees are highly encouraged to come to the office one day per week.
About the role
Reporting to the Director of Engineering, Maps & Data, this role will lead the AllTrails’ Data Science & Engineering team, a small and centralized engineering function that cross-functionally supports the business across product pods and partner teams including: data analytics, finance, and marketing. The team’s scope spans scalable data infrastructure, data governance, reliability, compliance, and the development and productionization of machine learning, AI, and algorithmic solutions that power internal operations and member-facing product experiences.
Due to high candidate volume and to ensure our hiring team has sufficient time to give every application the attention it deserves, we have set a closing date of August 20th for this position.
What you’ll be doing
Team Leadership & People Management
• 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.
Strategic Business Partnership & AI Strategy
• 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.
Data Platform & Engineering Execution
• 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.
Data Governance, Democratization & Platform Operations
• 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
What you bring
Team & People Leadership
• Leadership Experience: 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.
• Working Style & Values: 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.
Technical Architecture & Infrastructure
• Platform & Governance: Hands-on experience managing GCP data infrastructure, enforcing data governance, and maintaining production-grade data access patterns, storage, caching, and optimization standards.
• Modern Data Stack: 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.
• Pipeline & Processing: Proven expertise designing complex SQL/Python pipelines using parallelized processing frameworks (Dataflow, Spark, or similar) and core data modeling techniques (dimensional, star schema).
Engineering Practices & AI/ML Ops
• DevOps & SDLC: 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.
• Applied AI/ML: Track record of guiding teams through the full ML lifecycle (MLOps) to support applied use cases like recommendation, ranking, personalization, and classification engines.
Stakeholder Management & Execution
• Cross-Functional Ownership: Skilled at leading across both platform foundations and applied data products, with the business judgment to prioritize competing stakeholder needs.
• End-to-End Delivery: Proven ability to translate complex business requirements into technical specs, collaborating effectively with stakeholders to own projects from inception to completion.
Bonus Points
• 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)…
Source: Jobspresso (https://jobspresso.co/job/data-science-engineering-manager/)
Every day, our team works to get more people outside and help our members have better experiences on the trail. We lead with our values–positivity, innovation, humility, adaptability, inclusivity, and stewardship–in everything we do. Join us!
This is a U.S. based remote position. While this role does not require in-person attendance, we have a strong preference for candidates based in or near one of these cities: San Francisco CA, Portland OR, Seattle WA, Denver CO, and New York NY. Being in one of these cities creates opportunities to connect with teammates through Trail Days, co-working sessions, and local gatherings while still having the flexibility to work remotely. San Francisco employees are highly encouraged to come to the office one day per week.
About the role
Reporting to the Director of Engineering, Maps & Data, this role will lead the AllTrails’ Data Science & Engineering team, a small and centralized engineering function that cross-functionally supports the business across product pods and partner teams including: data analytics, finance, and marketing. The team’s scope spans scalable data infrastructure, data governance, reliability, compliance, and the development and productionization of machine learning, AI, and algorithmic solutions that power internal operations and member-facing product experiences.
Due to high candidate volume and to ensure our hiring team has sufficient time to give every application the attention it deserves, we have set a closing date of August 20th for this position.
What you’ll be doing
Team Leadership & People Management
• 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.
Strategic Business Partnership & AI Strategy
• 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.
Data Platform & Engineering Execution
• 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.
Data Governance, Democratization & Platform Operations
• 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
What you bring
Team & People Leadership
• Leadership Experience: 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.
• Working Style & Values: 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.
Technical Architecture & Infrastructure
• Platform & Governance: Hands-on experience managing GCP data infrastructure, enforcing data governance, and maintaining production-grade data access patterns, storage, caching, and optimization standards.
• Modern Data Stack: 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.
• Pipeline & Processing: Proven expertise designing complex SQL/Python pipelines using parallelized processing frameworks (Dataflow, Spark, or similar) and core data modeling techniques (dimensional, star schema).
Engineering Practices & AI/ML Ops
• DevOps & SDLC: 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.
• Applied AI/ML: Track record of guiding teams through the full ML lifecycle (MLOps) to support applied use cases like recommendation, ranking, personalization, and classification engines.
Stakeholder Management & Execution
• Cross-Functional Ownership: Skilled at leading across both platform foundations and applied data products, with the business judgment to prioritize competing stakeholder needs.
• End-to-End Delivery: Proven ability to translate complex business requirements into technical specs, collaborating effectively with stakeholders to own projects from inception to completion.
Bonus Points
• 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)…
Source: Jobspresso (https://jobspresso.co/job/data-science-engineering-manager/)
A t-xilen lu tzajni toj jun t-xnaq´tzb´il. Q´onxa t-xjel toj t-xaq u´j te t-xe´chil.
t-xe´chil: AllTrails
A t-xilen tzaj tqona AllTrails.
