Engineering Manager

The Backend Reporting squad builds and operates the data foundation that powers customer-facing reporting and data-driven decision-making at MNTN. The team transforms raw, log-level data into reliable, scalable, and durable analytics systems that serve as the single source of truth for enterprise reporting.

The squad owns the full lifecycle of reporting data across MNTN, including core performance metrics (Visits, Conversions, Impressions), incrementally, reporting-facing invoicing data, and all reporting ETL pipelines. This includes ownership of metric definitions and versioning, transformations across the Bronze, Silver, and Gold layers in BigQuery, all SQLMesh models and Airflow jobs, reporting databases such as ClickHouse, and the systems that ensure data quality, freshness, performance, cost efficiency, and reporting SLAs. The team is accountable for reporting correctness end-to-end, including automated testing, data contracts, incident response, and durable remediation.

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.

• 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.

• 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.

• 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.