Data Science & Engineering Manager

AllTrails is hiring a Data Science & Engineering Manager to lead a centralized team supporting engineering, analytics, finance, and marketing. The role combines hands-on data platform and pipeline engineering with team leadership and ownership of reliable, production-grade ML and MLOps systems.

Responsibilities:

• Lead and support a small team of talented data engineers and applied data scientists, fostering their professional growth and ensuring high performance through coaching, performance management, hiring, and team health assessments
• Partner with cross-functional stakeholders to understand their needs, translate them into requirements, prioritize work, and communicate progress, risks, and trade-offs across a wide range of business-critical and product-critical data initiatives
• Drive data governance, reliability, quality, cost-efficiency, privacy, compliance, and data access best practices across our data platform
• Oversee the systems and processes that keep data flowing reliably across the organization, including orchestration, transformation, alerting, and production pipelines
• Own and mature the team's MLOps practice including architecture for model deployment, versioning, monitoring, drift detection, evaluation, retraining, and rollback — turning prototype models into dependable, production-grade systems
• Champion the creation of tools to improve data and model cataloging, discoverability, documentation, and self-service to enhance overall democratization of data assets
• Troubleshoot complex data issues, performance bottlenecks, and system failures, providing timely and effective resolutions
• Create leverage for the broader organization by improving time-to-insight, time-to-detect, time-to-debug, and overall deployment velocity for data-related work
• Act as a thought partner on how AllTrails uses data, AI, and automation to improve internal productivity, trail content, map quality, personalization, and other member experiences
• Balance long-term platform investment with near-term delivery needs
• Architect, build, and maintain complex data pipelines for both batch and streaming workloads, moving data reliably to and from our data warehouse and operational systems; hands-on participation in research, diagramming, design reviews, code reviews, and on-call rotations is expected
• Research, evaluate, and recommend new data technologies, tools, and platforms to enhance our data capabilities and improve efficiency

Requirements:

• Please note that, at this time, we're unable to offer visa sponsorship for this role
• 5+ years of experience in data engineering or ML engineering, with at least 3+ years in managing high-performing data engineering, machine learning, and product-applied data science teams
• Comfort leading work across both platform foundations and applied data use cases, with the judgment to prioritize effectively across competing stakeholder needs
• Demonstrated, hands-on experience designing, building, and operating complex data pipelines, both batch and streaming, using tools such as Apache Airflow, Apache Spark, Apache Beam, Google Dataflow, or equivalent
• Multiple years of experience governing data infrastructure in Google Cloud Platform
• Extensive proficiency in transforming and governing high-volume datasets in industry-standard columnar SQL-based warehouses such as BigQuery (preferred) or Snowflake, preferably with experience in ELT tools like Dataform or dbt
• Proven expertise in leading the design, development, and maintenance of complex data pipelines using SQL and Python
• Solid understanding of best data modeling techniques (e.g. dimensional modeling, star schema, data normalization)
• Deep understanding and practical experience in data access patterns, storage solutions, caching, replication, and optimization techniques
• Proficiency in containerization and orchestration technologies (e.g., Docker, Kubernetes)
• Direct experience leading the software development lifecycle (SDLC) and continuous integration/continuous deployment (CI/CD) practices as they apply to both data pipelines and ML systems
• Proven expertise leading the full MLOps lifecycle: model deployment, versioning, monitoring, drift detection, retraining pipelines, and rollback strategies in a production environment
• Experience supporting AI/ML use-cases such as personalization, classification, ranking, recommendations, etc
• Willingness to be hands-on and participate in code reviews and an on-call support rotation (currently monthly)
• Demonstrated ability to collaborate effectively with diverse stakeholders, translate business requirements into detailed technical specifications, and own projects from inception through successful completion
• Strong people leadership skills, including coaching senior ICs, giving clear feedback, growing careers, and building an inclusive, accountable team culture
• Comfort operating in a fast-moving environment where you may need to zoom between strategic planning, stakeholder management, and hands-on technical decision-making
• Self-motivation and a deep sense of pride in your team's and your own work
• Comfort with ambiguity and an instinct for moving quickly while maintaining high standards
• Humility, empathy, and open-mindedness – a commitment to a collaborative, ego-free environment
• Passion for the outdoors
• This is a U.S. based remote position
• 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
• BigQuery (preferred)
• 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)