• Design, build, and own new backend services and APIs end-to-end, from technical proposal through implementation, testing, and production rollout. • Contribute to frontend experiences that surface the team’s services to customers • Take ambiguous or broadly-scoped problems and turn them into well-defined technical plans that the team can execute on. • Author design and architectural documents and use them to drive alignment across engineering and product teams • Collaborate with internal platform teams, product managers, and stakeholders across the organization the understand needs and deliver impactful solutions • Consider stability, performance, and scalability in your designs and in the guidance you provide to the team • Mentor and grow other engineers on the team through code review, pairing, and technical leadership • Participate in an on-call rotation

• Architect and own end-to-end integrations across Salesforce, Snowflake/BigQuery, Slack, Grafana, and billing & finance systems • Define and enforce integration patterns and API standards across the GTM stack • Lead incident response and root-cause analysis for integration failures; drive improvements to observability and alerting • Partner with Revenue Operations to translate business requirements into reliable, scalable technical solutions • Evaluate and rationalize third-party connectors, middleware, and iPaaS tooling against build-vs-buy criteria • Design and ship internal applications that replace SaaS point-solutions — reducing cost, consolidating workflows, and increasing team leverage • Establish a lightweight internal development practice: code standards, CI/CD, deployment patterns, and documentation norms • Leverage AI/LLM capabilities (APIs, agentic workflows) to accelerate internal tool development and automate repetitive operations work • Maintain a clear deprecation roadmap tied to measurable SaaS spend reduction targets • Set the technical direction for how GTM systems share data — defining canonical data models, source-of-truth ownership, and sync strategies • Conduct design reviews and provide architectural guidance to engineers, RevOps analysts, and technical stakeholders • Identify and remediate technical debt proactively; advocate for engineering rigor in a primarily non-engineering organization

• Build and maintain scalable mobile ad experiences using React Native and the Google Mobile Ads SDK for iOS and Android • Translate business and revenue requirements into scalable ad implementations across mobile applications • Design how ads are configured, loaded, and displayed while balancing performance, reliability, and user experience • Support multiple ad formats, including native, interstitial, and rewarded experiences • Collaborate with cross-functional partners to deliver high-quality mobile advertising solutions

• Design and build scalable distributed systems, backend platforms, compatible with AI/ML infrastructure for search, retrieval, ranking, recommendation, and personalization use cases • Manage scalable ML pipelines using Vertex AI Pipelines for training, evaluation and deployment to support ranking, retrieval, and recommendation personalization use cases • Develop and maintain data pipelines that support feature generation, model training, and analytics workflows • Own vector generation via Milvus, storage, and retrieval workflows • Implement model serving solutions using KServe and build APIs using FastAPI for low latency inference • Build observability and monitoring for models and pipelines • Track performance, drift, failures, and data quality issues • Collaborate with data scientists, product managers, and platform teams to define and deliver ML driven features • Investigate production issues across data pipelines, models, and services • Identify bottlenecks and improve reliability and performance • Create and maintain clear documentation for pipelines, models, APIs, and operational processes • Develop internal tools and dashboards to provide visibility into data processing and model behavior for stakeholders • Contribute to engineering standards, code quality, and best practices across Python-based services and ML systems • Stay current with ML infrastructure, MLOps practices, and relevant tools • Bring in improvements where they add clear value • Collaborate with product, data science, and frontend teams to deliver high quality search and feed experiences • Own production systems and debug issues across indexing, retrieval, ranking, and serving layers • Create clear documentation for pipelines, models, APIs, and system design