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Designs and maintains scalable backend data pipelines using distributed processing frameworks such as Apache Spark. Builds RESTful APIs, data integrations, model-serving pipelines, monitoring and observability systems, and optimized storage solutions across relational, NoSQL, and cloud data platforms. Partners with data scientists, machine learning engineers, and stakeholders on architecture and production infrastructure. Requires five years of relevant experience, including substantial experience with software development, cloud platforms, distributed systems, data pipelines, APIs, databases, and performance optimization.

Lead the design and evolution of high-performance feed definitions across a multi-cloud environment. Unify data ingestion and egress paths, maximize code reuse, minimize latency, and handle rapidly increasing data volumes. Drive self-service provisioning and platform flexibility while collaborating across global teams and fostering an inclusive engineering culture.

Represents the Marketplace Quality team on inventory quality and transparency issues; evaluates inventory partners for policy compliance; investigates client and vendor inquiries; analyzes platform-wide supply-quality data; develops technical, operational, policy, and business solutions; and manages sensitive stakeholder discussions across clients, partners, vendors, product, legal, partnerships, and commercial teams.

Leads strategic relationships with agency clients, drives portfolio revenue growth, collaborates on product development, and manages key client meetings. Coaches and develops a team of Account Managers, oversees account support and bandwidth, tracks performance goals, and partners with internal leaders to solve business challenges and improve organizational effectiveness.

Own and grow a portfolio of 7–12 existing advertiser and agency relationships. Develop account growth strategies, expand platform adoption, close strategic opportunities, lead executive reviews and negotiations, and drive revenue expansion. Build C-level relationships, provide digital advertising insights, manage forecasting and pipeline visibility, and collaborate cross-functionally with Client Services, Product, Marketing, and Revenue teams. Mentor peers and promote commercial excellence across the Growth organization.

Build and operate incident management tooling, observability infrastructure, logging systems, developer portal integrations, and alert-quality automation. Responsibilities include automating incident lifecycles, evaluating logging vendors and architectures, extending Backstage with Kubernetes and SLO capabilities, improving alert routing and grouping, and supporting production debugging across distributed systems. The role collaborates with engineers company-wide to improve reliability and on-call effectiveness.

Build, QA, and deliver custom audience segments using SQL and data tools; validate data quality and policy compliance; automate workflows; serve as primary stakeholder contact via Salesforce; troubleshoot and document issues; contribute to cross-regional enablement and process improvements.

Lead marketplace inventory quality efforts by evaluating partners, investigating policy inquiries, analyzing platform-wide supply data, and driving cross-functional solutions. Represent the team externally, craft strategic messaging, manage vendor relationships, and resolve complex inventory quality and transparency issues while influencing stakeholders across product, legal, partnerships, and commercial teams.

Build and improve analytics and observability tools that make event data accessible across the company. Own end-to-end features for event tracking and data pipelines, extend governed self-service access, provide technical guidance, partner with business and product teams, raise testing and quality standards, and mentor other engineers.

Design, build, and operate production-grade analytics systems and pipelines that deliver proactive signals and insights for Sales and Client Services. Translate business needs into scalable data models, automation, and integrated workflows; embed AI/LLM capabilities responsibly; maintain reliability, testing, documentation, and metric governance to drive commercial impact and better decision-making.