Technical Lead Manager, Data Engineering, Trust & Safety

We are hiring a Technical Lead Manager to lead and grow the Trust & Safety Data Engineering team. This is a hands-on leadership role for someone who can set strategy, shape data architecture, align senior stakeholders, coach engineers, and drive execution on high-impact data systems.

Responsibilities

Lead and grow a high-performing Trust & Safety Data Engineering team; Define the roadmap and technical strategy for Trust & Safety data systems; Build canonical, privacy-safe datasets and pipelines for abuse detection, fraud detection, risk signals, enforcement, scaled review, transparency reporting, and safety monitoring; Create reusable foundations for Trust & Safety model development, including features, labels, training data, backtesting, evaluation, and production inputs; Establish ownership, documentation, data quality standards, monitoring, and operational rigor for critical datasets and workflows; Reduce dependence on sensitive raw logs by building structured alternatives with appropriate access controls, retention, deletion semantics, and governance; Partner with Trust & Safety, Product, Policy, Privacy, Data Science, Engineering, and Data Platform on launch readiness, operational systems, and safety measurement; Raise the bar for technical judgment, prioritization, communication, and execution in a fast-moving environment

Requirements

Have 15+ years of experience in data engineering and have led data engineering teams that build and operate production data systems at scale; Experience in trust and safety, integrity, abuse prevention, fraud, investigations, risk operations, safety systems, privacy, or adjacent domains; Are deeply technical and comfortable with data architecture, modeling, pipelines, reliability, privacy, and operational tradeoffs; Have experience with large-scale data systems such as Spark, Airflow or similar orchestration systems, distributed storage, batch/streaming pipelines, and modern warehouse patterns; Think of data as a product: reliable, documented, governed, observable, discoverable, and designed for repeated use; Can create clarity in ambiguous problem spaces and make principled tradeoffs quickly; Have a strong track record partnering with senior stakeholders across engineering, data science, operations, policy, privacy, product, or executive teams; Have hired, developed, and retained senior engineers; Are motivated by building systems that make frontier AI products safer and more trustworthy

Nice to have

Experience supporting ML systems through feature engineering, training data, labels, model evaluation, or production model pipelines; Experience with launch readiness, monitoring, alerting, incident response, semantic layers, metrics governance, or executive-facing reporting

Benefits

Relocation assistance