Sr. Technical Program Manager, Data Engineering

Pinterest·San Francisco, CA, US; Remote, USRemotosenior

En corto

→Liderazgo de proyectos de modernización de plataformas de datos a gran escala en Pinterest.
→Trabajo diario con ingenieros, equipos de ML y análisis para migrar desde sistemas legacy a arquitecturas modernas (Kafka, Flink, Iceberg).
→Uso predominante de IA generativa (GenAI) para automatizar informes, tomar decisiones y gestionar riesgos en tiempo real.

Experience working with cross-functional teams in English is required.

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¿Qué piden?

✓Experiencia comprobada liderando proyectos técnicos de data engineering a gran escala.
✓Capacidad demostrada para gestionar dependencias entre múltiples equipos y sistemas.
✓Habilidad para comunicar decisiones técnicas a audiencias técnicas y ejecutivas.
✓Experiencia práctica con migraciones de plataformas de datos (ej. Hive a Iceberg, batch a streaming).
✓Conocimiento sólido de herramientas de orquestación y streaming (Kafka, Flink).
✓Adopción activa y responsable del uso de IA generativa en el trabajo diario.

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

KafkaFlinkIcebergCDCHiveData LakeWorkflow OrchestrationGenAIAI Coding Assistantsvibe coding

¿A quién escribirle en Pinterest?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

About Pinterest: Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible. At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI. Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here . The Team Pinterest's Data Engineering organization builds and operates the data platforms that power every Pinterest product — the batch and streaming pipelines that produce training data for our ML models, the storage and table formats that back our data lake, the workflow orchestration that runs it all, and the analytics platforms that fuel experimentation and decision-making. We're in the middle of a multi-year modernization effort: moving to streaming-first ingestion (CDC, Kafka, Flink), open table formats (Iceberg), a consolidated workflow platform, and retiring legacy footprints along the way. We work closely with ML, product, and analytics teams to make Pinterest's data platforms faster, more reliable, and more cost-efficient. What You'll Do: • Lead a multi-quarter portfolio of data platform modernization programs — spanning ingestion (CDC/Kafka/Flink), table format migrations (Hive to Iceberg), workflow consolidation, and legacy system deprecation — partnering with engineering leads across the Data Engineering organization. • Drive cross-team execution on migrations that involve many producer and consumer teams, managing timeline, dependency, and risk across service owners. • Build and maintain program dashboards and reporting that give engineering and Platforms leadership a clear, current view of progress, validated savings, risk, and decisions. • Own the reporting and tracking function for a large data infrastructure efficiency portfolio, coordinating with team-level DRIs and partner finance/InfraGov stakeholders. • Communicate technical tradeoffs and program status clearly to both technical and non-technical audiences, from individual engineers to senior leadership. • Use GenAI as the default operating model for EP PgM execution—producing AI-assisted first drafts of core program artifacts, modernizing high-toil workflows into AI-first mechanisms (e.g., intake triage, status synthesis, action/decision extraction, risk & dependency tracking), and synthesizing signals to proactively surface risks, decision/trade-offs, and escalation paths. • Prototype solutions to augment decisions through data (e.g. dashboards, data analysis) or simplify processes (e.g. process and workflow helpers, or internal tools) using AI coding assistants (“vibe coding”). • Follow Pinterest AI guidance for risk, governance, and safety-by-design: appropriately handle sensitive data, validate AI-generated outputs, document assumptions/limits, and ensure AI-assisted workflows meet applicable p

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