Data Science & Engineering Manager

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Science & Engineering Manager based in the United States.

As a Data Science & Engineering Manager, you will lead a centralized team responsible for critical data, engineering, and applied AI capabilities across the organization.
You will shape the strategy and execution of scalable data infrastructure, machine learning systems, governance, and analytics foundations.
The role combines people leadership with hands-on technical guidance, requiring you to balance long-term platform investments with immediate business priorities.
You will partner closely with product, analytics, finance, marketing, and engineering leaders to turn complex business needs into impactful technical solutions.
Your work will directly influence data reliability, operational efficiency, personalization, content quality, and AI-powered experiences.
You will also champion modern engineering practices, data democratization, governance, observability, and continuous improvement across the data ecosystem.
This is a remote U.S.-based leadership opportunity offering meaningful ownership, technical influence, and the chance to build a high-performing and inclusive team.

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.

How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

#LI-CL1

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.