Manager, Engineering - Brands Team

Applicants must be currently authorized to work in the United States. iSpot is not able to sponsor or take over sponsorship of an employment visa for this position at this time.

iSpot competes for the best talent. Our compensation packages consist of salary and equity in one of Seattle's hottest start-ups, as well as other standard benefits. Most importantly, we provide a really interesting working experience, and the chance to contribute to the success of something great.

What You'll Be Part Of:
We are seeking an Engineering Manager to lead our high-scale Brands Team - the mission-critical systems that ingest, process, and organize massive streams of media data. You will own the end-to-end data lifecycle, from ingestion of raw broadcast/digital signals to the management of a robust Content Catalog, with explicit accountability for software quality, the frontend, and the ad catalog. Your objective is to ensure our platform provides a highly accurate, performant, "source-of-truth" foundation for all downstream measurement and attribution products.

Delivering committed projects on the timelines we commit to is the single most important measure of success in this role. Responsibilities:
Platform & Data

• Lead the strategy for upgrading and refactoring ingestion and cataloging systems to improve throughput, reduce latency, and pay down technical debt.
• Embed machine learning and AI into the content cataloging workflow to automate creative identification, metadata extraction, and fingerprinting at scale.
• Ensure the accuracy and reliability of massive ad datasets, partnering cross-functionally to evolve platform capabilities in support of complex attribution modeling.
• Own onboarding and maintenance of large third-party data sources (ACR, set-top-box, streaming, panel signals), including migration and cutover work when providers change.
• Own the architecture and usability of internal-facing tools and dashboards for internal power users, even though the team's focus is backend-heavy.

Lifecycle & Quality

• Own the pipeline from design through release and post-release stability, including active monitoring to catch anything that diverges from expectation.
• Define and enforce quality standards as the final authority on release readiness, tracked via defect density, test coverage, and production uptime. Ensure unit, integration, and regression testing are natively embedded in CI/CD.
• Lead root-cause analysis on production incidents and coach the team in doing their own. Document risks as they're identified, maintain contingency plans, and flag slippage the moment it becomes visible.
• Ensure 24/7 availability of the ad catalog and ingestion engines, and own outage resolution whenever your team is involved.

Team & Delivery

• Translate platform strategy into well-scoped sprints, balancing new feature delivery against maintenance of "always-on" systems. Own the team's daily operating cadence - standup, backlog triage, and continuous sprint tracking.
• When something ambiguous or hard lands on the team, take a position and drive it rather than escalating for direction. Bring decisions upward with a recommendation attached.
• Coach and mentor engineers, set written development goals per direct report (behavioral, leadership, execution, technical), and identify areas of the system held by only one person - closing those gaps through deliberate work assignment and pairing.
• Find leaders within the team and lean on them; distributing leadership builds resilience and growth paths for others.
• Champion AI-assisted development practices across the team, holding the same quality and review bar that applies to all shipped code.
• Own the cost of development work within your team's scope, and monitor contribution balance sprint over sprint, intervening early when imbalance shows up.
• Surface technology and operational improvements with a scope attached, not just an unscoped suggestion.

Qualifications and

Education Requirements:

• Proven track record managing high-performing engineering teams delivering production-grade, distributed software at scale, with a history of hitting committed delivery timelines.
• Deep domain expertise in big data architectures - high-throughput ingestion, media metadata systems, or comparable large-scale data platforms.
• Experience embedding AI/ML into production workflows for automated tagging, identification, or content classification.
• Architectural fluency across both backend data systems and the frontend technologies used to build internal tools.
• Experience defining and enforcing quality gates (automated testing frameworks, release criteria) and driving RCA-based operational improvement.
• Strong sprint-planning discipline that balances feature velocity with infrastructure stability and data integrity.
• Experience building structured, multi-dimensional growth plans for engineers and reducing single-person coverage risk across a team.
• Excellent cross-functional communication with Product, Data Science, and Operations.

Preferred

Skills:

• Ad tech, media…