VP of Engineering

You Will Be

• Leading, coaching, and holding accountable engineering managers and individual contributors across our application stack, data pipelines, quality engineering, and cloud platform.

• Build technical leaders that reduces single points of failure and allows the organization to scale without depending on individual heroics.

• Set and reinforce a high bar for technical judgment, delivery ownership, collaboration, documentation, operational discipline, and customer impact.

• Own engineering capacity planning, staffing allocations, and the engineering operating budget. Manage cloud, data, AI, tooling, and software vendor spend.

• Partner with Product, UX, Data Science, and the Chief Product Officer to shape the Polaris roadmap, sequence investments, expose dependencies, and make clear tradeoffs among customer value, speed, reliability, cost, and technical health.

• Translate agency, client, and market needs into a coherent technology strategy across Polaris IQ, shared data products, and measurement applications.

• Maintain an actionable architecture roadmap that supports product growth while retiring fragile systems, duplicated capabilities, and unnecessary operational cost.

• Define and manage an engineering scorecard that tracks delivery speed and quality. Continue moving delivery toward more frequent deployments through automated testing, CI/CD, observability, release automation, and fast feedback loops.

• Set and execute the engineering AI strategy. Evaluate coding agents, model providers, agent runtimes, and automation opportunities based on measurable value, security, reliability, usability, and total cost.

• Own the end-to-end technical architecture across Python and Django services, React applications, PostgreSQL, Airflow, BigQuery, dbt, GKE, Cloud Run, Terraform, GitOps, Argo CD, and GitHub Actions.

• Ensure data pipelines and systems deliver accurate, timely, and observable data across advertising, analytics, commerce, and client data sources.

• Own service-level objectives, incident management, corrective actions, capacity planning, and risk reduction for application, platform, and data workloads.

• Act as the final engineering escalation for critical incidents and high-risk architecture decisions, while ensuring durable runbooks, monitoring, and delegated ownership exist afterward.

• Partner with Security and Legal on secure architecture, identity and access management, secrets, vendor risk, data governance, incident response, client assurance, and compliance obligations.

You Must Have

• 12+ years of software engineering experience, including 5+ years leading engineering managers and multidisciplinary engineering organizations.

• A record of building healthy teams, developing leaders, raising accountability, and delivering complex products in close partnership with Product, UX, Data Science, Security, and business stakeholders.

• Deep architectural judgment across modern SaaS applications, APIs, cloud infrastructure, data platforms, distributed systems, CI/CD, observability, and security. Direct experience with GCP and the Polaris stack is valuable, but equivalent depth in comparable technologies is acceptable.

• Experience improving developer productivity and delivery performance through platform capabilities, automation, testing, operational standards, and reporting on DevEx KPIs.

• Practical experience adopting AI-assisted development or agentic workflows, with the judgment to balance speed against security, quality, cost, and maintainability.

• Demonstrated ownership of cloud and software budgets, vendor relationships, capacity planning, and technical contract decisions.

• Calm, decisive leadership and the ability to move between executive communication and hands-on technical investigation when necessary.

• Clear written and verbal communication, especially when explaining risk, architecture, cost, or tradeoffs to clients and non-technical leaders.

• A bias toward durable systems, explicit ownership, and delegation. This role must solve urgent problems without allowing the organization to remain dependent on a single person.

Nice to Have

• Digital marketing, advertising technology, agency technology, or high-volume data and analytics integrations.

• Customer-facing, multi-tenant enterprise products that share data, services, or platform capabilities.

• Data ingestion and transformation at scale, including heterogeneous APIs, normalized data models, orchestration, warehouse design, and data quality controls.

• Production support for data science and measurement applications such as marketing mix modeling, forecasting, experimentation, optimization, or anomaly detection.

• SOC 2 or similar control environments, enterprise identity and access management, and client security reviews.