Staff Data Engineer

MNTN is a company that prioritizes its people and is recognized for its innovative advertising solutions in Connected TV. They are looking for a Staff Data Engineer to lead the technical architecture of their reporting data platform, ensuring data quality and performance while mentoring other engineers.

Responsibilities:

• 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

Requirements:

• 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