Sr. Technical Program Manager, Data Engineering

Pinterest•San Francisco, CA

8d•Remote

About The Position

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.

Requirements

5+ years of TPM experience leading complex, cross-functional technical programs, ideally within large-scale data platforms, streaming systems, or data infrastructure modernization efforts.
Solid understanding of the principles behind large-scale data platforms — batch/streaming pipelines, table formats, storage layers, workflow orchestrators — and the tradeoffs involved in modernizing them.
Excellent analytical and problem-solving skills, with experience building dashboards, interpreting data, and using insights to drive decisions and validate savings.
Strong collaboration and communication skills, with a track record of partnering with senior engineering and business stakeholders in high-visibility settings.
Comfort with ambiguity and shifting priorities across a fast-moving, multi-workstream roadmap.
Bachelor’s degree in Computer Science, Engineering, a related field or equivalent experience.

Nice To Haves

Experience with data platforms, streaming systems (Kafka, Flink), or open table formats (Iceberg) is a plus; willingness to learn and grow across domains is essential.
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)
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.

Responsibilities

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 policy/compliance expectations before broad adoption.