Data Science & Engineering Manager

Team: IT

Location: US

Commitment: Full-time

Workplace Type: remote

Accountabilities:

• Lead, hire, coach, and develop a team of data engineers and applied data scientists, fostering an inclusive, accountable, collaborative, and high-performing culture.
• Establish team priorities, support career development, coach senior individual contributors, and manage performance and team health.
• Partner with cross-functional leaders to translate business objectives into technical requirements, prioritize initiatives, and balance strategic platform investments with near-term delivery.
• Serve as a strategic thought partner on the use of data, AI, machine learning, and automation to improve internal productivity and member-facing experiences.
• Drive the architecture and delivery of scalable batch and streaming data pipelines and oversee the productionization of machine learning and algorithmic systems.
• Oversee data orchestration, transformation, alerting, and organizational data flows, resolving complex performance issues and system failures to maintain reliable production systems.
• Champion data quality, reliability, privacy, compliance, governance, access controls, and cost efficiency across the data platform.
• Develop data cataloging, documentation, and self-service capabilities that make high-quality data more accessible across the organization.
• Improve time-to-insight, debugging efficiency, deployment velocity, and overall engineering productivity through continuous evaluation of tools, technologies, and processes.
• Guide teams through the full machine learning lifecycle, including MLOps and applied use cases such as recommendation, ranking, personalization, and classification.
• Translate complex business requirements into clear technical specifications and lead projects from initial definition through successful delivery.
• Provide technical guidance on modern engineering practices, including CI/CD, observability, infrastructure, code quality, and production operations.

Requirements

• 5+ years of professional experience in data engineering, with 3+ years managing data engineering, machine learning, or data science teams.
• Demonstrated experience coaching senior individual contributors, developing talent, and building inclusive, accountable engineering teams.
• Strong hands-on experience managing GCP-based data infrastructure, data governance, production data access, storage, caching, and optimization.
• Extensive experience working with modern data platforms, including high-volume datasets in BigQuery or Snowflake and ELT technologies such as Dataform or dbt.
• Strong knowledge of orchestration technologies such as Apache Airflow and experience designing complex SQL and Python data pipelines.
• Proven expertise with parallelized data processing frameworks such as Dataflow, Spark, or comparable technologies.
• Solid understanding of data modeling techniques, including dimensional modeling and star schemas.
• Familiarity with software engineering and DevOps practices, including Docker, Kubernetes, CI/CD, monitoring and observability tools, code reviews, and production on-call practices.
• Experience guiding teams through MLOps and the end-to-end machine learning lifecycle, particularly for recommendation, ranking, personalization, or classification applications.
• Strong stakeholder management and business judgment, with the ability to prioritize competing demands and lead across platform engineering and applied data initiatives.
• Proven ability to communicate complex technical concepts clearly and collaborate effectively with technical and non-technical stakeholders.
• Adaptable, self-motivated, empathetic, and humble leadership style, with the ability to operate effectively in fast-paced and ambiguous environments.
• Experience with geospatial data, mapping, tiling, graph databases, routing, multi-cloud environments, B2C businesses, or infrastructure-as-code tools such as Terraform is a plus.

Benefits

• Competitive base salary of $170,000–$215,000 USD.
• Equity participation and performance-based bonus opportunities.
• Comprehensive medical, dental, and vision coverage.
• Unlimited paid time off in addition to company holidays.
• Fully paid parental leave for birthing and non-birthing parents.
• 401(k) matching and access to financial wellness resources.
• Remote work stipend to support a comfortable and productive home office.
• Annual learning and professional development stipend.
• Monthly company-wide no-meeting days dedicated to testing and improving the product.
• Exclusive discounts on subscriptions and merchandise for employees, friends, and family.
• Fully remote work within the United States, with a preference for candidates near San Francisco, Portland, Seattle, Denver, or New York for opportunities to connect with colleagues.
• Additional opportunities for in-person collaboration through team gatherings, coworking sessions, and local events.
• A collaborative and inclusive environment focused on professional growth, personal well-being, and meaningful work.