Staff Software Engineer

At MNTN, we put our people first, full stop. This allows our company culture to be defined by our team members and their shared values, like trust, ambition, quality, radical honesty, and compassionate leadership. It’s why we all really love working for the Hardest Working Software in Television™ (and also why we were named one of Ad Age’s Best Places To Work in 2025.)

We pride ourselves on bringing unrivaled performance and simplicity to Connected TV advertising. Our self-serve technology makes running TV ads as easy as search and social, helping brands drive measurable conversions, revenue, site visits, and more. It’s what led MNTN to being named one of Fast Company's Most Innovative Companies in 2023. You can learn more about us and everything we do by visiting https://mountain.com/.

We’re committed to innovation that empowers, not replaces. At MNTN, AI is a tool for growth, enhancing efficiency while keeping a people-first approach. Our goal is to streamline workflows and drive new solutions—without compromising the human element that makes our company great.

So if wanting to do more, own more, and make a bigger impact comes naturally to you, then you may be the person we're looking for to join us in our next stage of growth.

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 wit