This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are looking for a Backend Reporting Engineering Manager to lead a team of Data Engineers and Analytics Engineers responsible for the systems that power all reporting analytics at MNTN. This is a hands-on engineering leadership role focused on building and operating production-grade data systems with real SLAs, cost constraints, and failure modes. The ideal candidate has deep experience in Data Engineering, Analytics Engineering or SRE-adjacent roles in production and brings a strong bias toward automation, reliability, and operational excellence. While this role is platform-heavy, it also carries meaningful Product ownership and stakeholder-facing responsibility. The manager is expected to represent the reporting platform with clarity and credibility, translate business needs into durable system design, and ensure that reporting capabilities evolve in lockstep with customer and business requirements. What you'll do - Lead, mentor, and grow a team of Data Engineers and Analytics Engineers responsible for MNTN’s reporting foundation. - Own all analytics business logic and data engineering systems that power reporting, including: - Metric definitions, computation logic, versioning, and change management - ETLs and transformations across the medallion architecture (Bronze, Silver, Gold) - Data quality, reconciliation, validation, and automated testing frameworks - Act as the primary owner of reporting correctness, ensuring consistent definitions and behavior across all downstream consumers. - Establish and enforce data contracts, freshness guarantees, and reporting SLAs in partnership with Product and customer-facing teams. - Drive the design and evolution of reporting schemas optimized for performance, cost efficiency, and ease of extension as requirements change. - Build systems and guardrails that prevent regressions, reduce manual QA, and replace brittle, one-off logic with standard patterns. - Lead incident response for reporting data issues, including stakeholder communication, root cause analysis, and durable remediation. - Partner closely with Program, UI, and API teams to: - Shape reporting requirements early - Clarify tradeoffs between speed, accuracy, and complexity - Support new customer-facing insights without compromising platform stability - Represent the Backend Reporting squad in cross-functional forums, providing clear status, risks, and recommendations to stakeholders. - Continuously improve performance tuning and cost optimization of analytics workloads, particularly in BigQuery. - Set and uphold a high bar for operational rigor, testing discipline, and production readiness across the team. What success looks like - Faster, safer iteration on metrics and KPIs. - Low data incident rates and fast MTTR when issues occur. - Consistent adherence to reporting SLAs. - Measurable reduction in manual QA and rework. - Predictable cost and performance characteristics of analytics workloads. - High trust in reporting data across Product, Engineering, and the business. Qualifications - Proven experience managing Data Engineers, Analytics Engineers or SRE-adjacent Engineers building scalable production data platforms, not just consuming data. - Strong background in data engineering, distributed systems, or platform infrastructure. - Demonstrated ability to balance platform rigor with Product ownership, including shaping requirements and setting expectations with stakeholders. - Deep understanding of SQL-based analytics systems, including schema evolution, backfills, and versioned transformations. - Experience operating data pipelines with defined SLAs for freshness, correctness, and performance. - Hands-on familiarity with modern analytics tooling (e.g., SQLMesh, dbt-class frameworks, Airflow-class orchestration). - Track record of reducing operational toil through automation, standardization, and system-level design. - Experience with data observability, testing frameworks, and incident management in production environments. - Strong stakeholder-facing communication skills, with the ability to explain complex data behavior clearly to non-technical audiences. - Execution-oriented leadership style: you prioritize clarity, accountability, and outcomes over process theater. Benefits - 100% remote within the US - Flexible vacation policy - Annual vacation allowance for travel related expenses - Three-day weekend every month of the year - Competitive compensation - 100% healthcare coverage - 401k plan - Flexible Spending Account (FSA) for dependent, medical, and dental care - Access to coaching, therapy, and professional development

• Own execution of the engineering strategy • Partner closely with the CTO and cross-functional leaders to translate company priorities into clear plans and delivery outcomes. • Lead and support multiple engineering teams • Provide leadership across squads, ensuring teams are aligned, unblocked, and consistently delivering meaningful business impact. • Build high-performing teams through hiring and coaching • Recruit, onboard, and retain top engineering talent, while developing engineers and managers through feedback, mentorship, and career support. • Create clarity, structure, and accountability • Help teams navigate ambiguity, set goals, define success metrics, and improve predictability of delivery. • Drive engineering excellence and pragmatic technical decision-making • Support technical direction, guide trade-offs, and ensure teams build scalable and maintainable systems. • Strengthen collaboration across Product, Design, and Data • Foster strong partnerships across functions to ensure smooth execution and shared ownership of outcomes. • Champion culture and ways of working • Promote psychological safety, continuous improvement, and an environment where engineers can do their best work.

• Own the research and intelligence direction of the AI system. • Define how AI reasons, evaluates, and improves in a product used with high frequency. • Set and evolve the research direction for core intelligence, including context representation, memory, reasoning, planning, and orchestration. • Decide when to design new model architectures versus adapting or leveraging frontier open-source or commercial models. • Define evaluation frameworks that measure real-world usefulness, robustness, safety, and long-term behavior – not benchmark vanity. • Own alignment, safety, and guardrail strategy as first-class product concerns. • Guide exploration of frontier techniques such as retrieval-augmented training, mixture-of-experts, distillation, multi-agent orchestration, and multimodal systems. • Shape early product intelligence direction in close partnership with product and application engineering. • Set the technical bar for research rigor, judgment, and taste across the organization.

• Own the application engineering strategy and execution across backend, mobile, and desktop. • Lead and grow a small, senior applications team, including backend, mobile, and desktop engineers. • Set the architectural direction for AI-powered product workflows, APIs, and client integrations. • Ensure AI capabilities are integrated into the product with clear abstractions, predictable behavior, and graceful failure modes. • Partner closely with Machine Learning leadership to translate model capability into shippable product features. • Make high-impact decisions across latency, cost, reliability, security, and user experience. • Establish a strong execution culture through code reviews, design reviews, and hands-on technical leadership. • Ensure production readiness: observability, monitoring, retries, fallbacks, privacy, and cost controls. • Balance speed and discipline – shipping quickly without compromising long-term system quality.