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AllTrails

Digital trail guides and maps platform

Data Science & Engineering Manager

Engineering LeadershipPosted on Nov 14
Full-timeRemote in USARemote

About the Job

Requirements

  • At least 5 years of experience in data engineering or machine learning engineering, including at least 3 years managing data engineering, machine learning, and product-applied data science teams.
  • Experience leading work across platform foundations and applied data use cases and prioritizing competing stakeholder needs.
  • Hands-on experience designing, building, and operating complex batch and streaming data pipelines using Apache Airflow, Apache Spark, Apache Beam, Google Dataflow, or equivalent tools.
  • Multiple years of experience governing data infrastructure in Google Cloud Platform.
  • Experience transforming and governing high-volume datasets in columnar SQL-based data warehouses such as BigQuery or Snowflake, preferably with ELT tools such as Dataform or dbt.
  • Experience leading the design, development, and maintenance of complex data pipelines using SQL and Python.
  • Understanding of data modeling techniques, including dimensional modeling, star schema, and data normalization.
  • Understanding and practical experience with data access patterns, storage solutions, caching, replication, and optimization techniques.
  • Proficiency in containerization and orchestration technologies such as Docker and Kubernetes.
  • Experience leading the software development lifecycle and continuous integration/continuous deployment practices for data pipelines and machine learning systems.
  • Experience leading the MLOps lifecycle in production, including model deployment, versioning, monitoring, drift detection, retraining pipelines, and rollback strategies.
  • Experience supporting AI and machine-learning use cases such as personalization, classification, ranking, and recommendations.
  • Willingness to participate in code reviews and a currently monthly on-call support rotation.
  • Ability to collaborate with diverse stakeholders, translate business requirements into technical specifications, and own projects from inception through completion.
  • People leadership skills, including coaching senior individual contributors, giving clear feedback, growing careers, and building an inclusive, accountable team culture.
  • Ability to shift between strategic planning, stakeholder management, and hands-on technical decision-making.
  • Passion for the outdoors.

Responsibilities

  • Lead and support a small team of data engineers and applied data scientists through coaching, performance management, hiring, and team health assessments.
  • Partner with cross-functional stakeholders to understand needs, translate them into requirements, prioritize work, and communicate progress, risks, and trade-offs across business-critical and product-critical data initiatives.
  • Drive data governance, reliability, quality, cost-efficiency, privacy, compliance, and data access best practices across the data platform.
  • Oversee orchestration, transformation, alerting, and production pipelines that keep data flowing reliably across the organization.
  • Own and mature the team's MLOps practice, including architecture for model deployment, versioning, monitoring, drift detection, evaluation, retraining, and rollback, and productionize prototype models into dependable systems.
  • Create tools to improve data and model cataloging, discoverability, documentation, and self-service.
  • Troubleshoot complex data issues, performance bottlenecks, and system failures and provide timely resolutions.
  • Improve time-to-insight, time-to-detect, time-to-debug, and deployment velocity for data-related work across the organization.
  • Advise on using data, AI, and automation to improve internal productivity, trail content, map quality, personalization, and member experiences.
  • Balance long-term platform investment with near-term delivery needs.
  • Architect, build, and maintain complex batch and streaming data pipelines that move data to and from the data warehouse and operational systems; participate hands-on in research, diagramming, design reviews, code reviews, and on-call rotations.
  • Research, evaluate, and recommend data technologies, tools, and platforms to enhance data capabilities and efficiency.

Desired Qualifications

  • Experience working with geospatial data, maps, tiling, graph databases, or routing.
  • Experience working in a multi-cloud environment using Google Cloud Platform and Amazon Web Services.
  • Experience working at a business-to-consumer company.
  • Experience managing infrastructure as code, such as Terraform.
  • Experience with BigQuery is preferred.

About the Company

AllTrails

Digital trail guides and maps platform

AllTrails provides a digital platform with hand-curated trail guides and maps for outdoor activities like hiking, camping, and running. It runs on a mobile app and website where users access trail maps, reviews, photos, and recorded activities. The service is powered by a community of over 20 million contributors, which enables filtering, saving favorites, and building bucket lists. Revenue comes from a freemium model with a premium AllTrails Pro that adds offline maps, real-time overlays, and more detailed trail information. The company differentiates itself through its large, active user community, offline capabilities, and live overlays, plus its commitment to environmental conservation via the 1% for the Planet donation. Its goal is to help people discover, plan, and enjoy outdoor adventures with reliable information while supporting conservation and outdoor participation.

Company size
201-500
Company stage
Growth Equity (Non-Venture Capital)
Total funding
$227.8M
Headquarters
San Francisco, CA
Founded
2010

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