• 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

• Manage the use of computational resources of the server platform, in coordination with network management, ensuring availability and adequate performance; • Install and configure Linux operating systems, components, and services, including high-availability environments; • Diagnose and resolve incidents and problems in operating systems and critical services, ensuring continuity in high-availability scenarios; • Update operational environments by applying fixes and patches released by vendors; • Define and structure resources to be accessed on servers (folders, shares, permissions); • Establish naming conventions and organization of users and groups in accordance with security policies; • Perform basic configuration and provide support for content filtering solutions (proxy); • Perform basic configuration and provide support for infrastructure monitoring solutions; • Participate in projects, providing technical information on sizing and specifications of servers, their components, and software; • Prepare technical documentation and internal articles on best practices for using and administering the Linux platform in high-availability environments; • Provide support for hardware- and operating system-related issues on servers; • Perform basic configuration and provide support for firewall and IPS solutions; • Follow up on technical tickets with vendors to resolve complex issues related to the Linux platform; • Research solutions and prepare technical specifications to support analyses and decisions related to Linux server infrastructure.