Staff Software Engineer

The Backend Reporting squad builds and operates the data foundation that powers customer-facing reporting and decision-making at MNTN. We own the full lifecycle of reporting data, from raw log-level ingestion to trusted, production-grade analytics systems that serve as the single source of truth for enterprise reporting. This includes core performance metrics, incrementality, invoicing data, medallion-layer transformations in BigQuery, SQLMesh models, Airflow orchestration, ClickHouse serving, and the systems that enforce data quality, freshness, performance, cost efficiency, and reporting SLAs. The team is accountable for reporting correctness end to end.

We are hiring a Staff Software Engineer in Data Engineering to act as a senior technical leader on this platform. This is a hands-on role focused on architecting and evolving production-grade data systems with real SLAs and cost constraints. You will set technical standards, drive architectural decisions, and partner with Engineering and Product to ensure the reporting foundation scales without becoming brittle or inconsistent.

What you’ll do

• Own the technical architecture of the reporting data platform across ingestion, transformation, serving, and observability layers.
• Design and evolve metric computation systems, including versioning strategies, backfills, schema evolution, and change management that preserve correctness and stakeholder trust.
• Lead the design of transformations across the medallion architecture in BigQuery, ensuring performance, cost efficiency, and clarity of ownership.
• Define and enforce engineering standards for SQLMesh models, orchestration patterns, testing coverage, and code review quality.
• Build and mature automated data quality frameworks, reconciliation systems, and data contracts that prevent regressions and detect anomalies early.
• Act as the technical lead during reporting incidents, driving root cause analysis and implementing durable systemic fixes rather than one-off patches.
• Partner with UI, API, and Program teams to shape reporting requirements early and translate ambiguous business asks into clear, durable system design.
• Continuously improve query performance, storage patterns, and workload optimization in BigQuery and ClickHouse to maintain predictable cost and latency.
• Identify architectural risks and technical debt early and create pragmatic, staged plans to remediate them without stalling product velocity.
• Mentor and level up Data Engineers and Analytics Engineers through design reviews, pairing, and by setting a high bar for production readiness.

What success looks like

• Faster, safer iteration on metrics and KPIs
• Reporting metrics are consistent, versioned, and trusted across all downstream consumers.
• Data incidents are rare, and when they occur, the systems in place make detection and remediation fast and methodical.
• Schema changes and new metric launches happen with minimal regression risk.
• Analytics workloads have predictable cost and performance characteristics.
• Clear technical standards exist for modeling, orchestration, and testing, and the team consistently adheres to them.
• The reporting platform evolves without accruing brittle logic or unowned complexity.

What you’ll bring

• 8+ years of experience in software or data engineering, including significant ownership of production analytics platforms.
• Deep experience designing and operating large-scale data platforms in production environments with explicit SLAs for freshness, correctness, and performance.
• Strong expertise in SQL-based analytics systems, including complex transformations, backfills, schema evolution, and metric governance.
• Hands-on experience with modern analytics tooling such as SQLMesh or dbt-class frameworks, Airflow-class orchestration, and columnar data warehouses like BigQuery.
• Demonstrated ability to design for operability, including observability, alerting, testing strategies, and incident management.
• Strong systems thinking. You understand how upstream event design, transformation logic, serving layers, and cost models interact.
• A track record of reducing operational toil through automation, standardization, and clear architectural patterns.
• Ability to influence technical direction across teams without formal authority, using clear reasoning and strong design artifacts.
• Comfort operating in ambiguous product spaces where requirements evolve and tradeoffs between speed, accuracy, and complexity must be made explicit.
• A pragmatic mindset. You care about long-term durability, but you also know how to sequence work so the platform improves without blocking the business.