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 Own the strategy and execution of a multi-year portfolio of data platform modernization programs — spanning ingestion (CDC/Kafka/Flink), table format migrations (Hive to Iceberg), workflow consolidation, and legacy system deprecation — in partnership with engineering leaders across Data Engineering Lead migrations across multiple orgs involving many producer and consumer teams: anticipate technical failure points early, resolve core technical uncertainties with engineering, and settle cross-org priority conflicts Define the portfolio’s metrics, governance, and reporting mechanisms so engineering, Platforms leadership, and Finance have a clear, current view of progress, validated savings, risk, and decisions Own a large data infrastructure efficiency portfolio end to end, from savings strategy and forecasting to validation with team-level DRIs, Finance, and InfraGov Influence senior leadership (up to VP/e-staff) with clear recommendations on technical tradeoffs, investment, and sequencing, and drive consensus across orgs Mentor TPMs and engineering DRIs, and create best practices, tools, and methodologies that scale program management across the Platforms organization 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 policy/compliance expectations before broad adoption A track record of influencing senior engineering and business leadership in high-visibility settings, driving consensus, and delivering difficult messages when needed A track record of mentoring TPMs or engineers and building mechanisms that others adopt Workflow design, AI fluency, data & insights orientation: experience turning repeatable program work into durable, low-toil mechanisms and improving decision-making by using GenAI (e.g., strong prompting, vibe coding lightweight scripts/tools, dashboards, data analysis and leveraging agents where appropriate) Advanced understanding of large-scale data platforms — batch/streaming pipelines, table formats, storage layers, workflow orchestrators — and the ability to anticipate technical risk and influence architectural and roadmap decisions A track record of defining program metrics and targets, building forecasting and savings models, and using data to drive investment decisions 8+ years of TPM experience leading complex, cross-functional technical programs, including strategic, multi-org programs or portfolios within large-scale data platforms, streaming systems, or data infrastructure modernization Experience with streaming systems (Kafka, Flink), open table formats (Iceberg), or large-scale storage migrations is strongly preferred Comfort leading through ambiguity across all levels of leadership, making high-quality decisions with incomplete information Safety-by-design AI fluency: experience operating within AI governance expectations (risk assessment, data handling, model/output validation, auditability/traceability) and proactively identifying where AI use is not appropriate or requires additional controls Bachelor’s degree in Computer Science, Engineering, a related field or equivalent experience